{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":175,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":175,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"77b7d61c5f5f","filters":{"topic":"Digital Media Forensic Detection"}},"results":[{"id":"W2407561938","doi":"10.1109/lsp.2015.2438008","title":"Median Filtering Forensics Based on Convolutional Neural Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":418,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Science Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pooling; Image (mathematics); Pattern recognition (psychology); Filter (signal processing); Median filter; Residual; Computer vision; Image processing; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.02658748460942762,"gpt":0.2257852914226731,"spread":0.1991978068132455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834266,0.0002164049,0.0001717878,0.0001607201,0.0001212804,0.0003942377,0.0004980103,0.0000698335,0.000002434219],"category_scores_gemma":[0.00004628791,0.0002077303,0.00006734312,0.00042193,0.000163224,0.0008974693,0.00005210405,0.0002718543,0.00002278698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001509649,"about_ca_system_score_gemma":0.0001212077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007152374,"about_ca_topic_score_gemma":0.000003009493,"domain_scores_codex":[0.9981369,0.00004813803,0.0002414537,0.0004335734,0.0006871921,0.0004527598],"domain_scores_gemma":[0.9991267,0.0001062456,0.0001293879,0.0002565988,0.0001083302,0.0002727446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005304758,0.00004557649,0.0001161037,0.00002553958,0.000009576118,0.0001138468,0.0002603242,0.6151883,0.00189633,0.0001364069,0.00961211,0.3725428],"study_design_scores_gemma":[0.0004537779,0.0001439909,0.00009758949,0.00006853505,0.000004818299,0.00002957104,0.000007933323,0.9958655,0.002108494,0.0006269502,0.0003485985,0.0002442499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01870868,0.00002045818,0.9741956,0.003921425,0.002147004,0.0001142091,0.000001666406,0.0003332217,0.0005577479],"genre_scores_gemma":[0.9772369,8.350712e-8,0.01389269,0.008185061,0.0006271563,0.0000167621,0.000007199541,0.00002214122,0.00001205827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9603029,"threshold_uncertainty_score":0.8470995,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297988790","doi":"10.3390/app12199820","title":"A Novel Deep Learning Approach for Deepfake Image Detection","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Convolutional neural network; Face (sociological concept); Transfer of learning; Artificial intelligence; Deep learning; Fake news; Social media; Computer security; Data science; Internet privacy; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01717738125733426,"gpt":0.2232634776273456,"spread":0.2060860963700114,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009847794,0.0001163255,0.0001151608,0.0001942833,0.001219745,0.0003420712,0.0008813753,0.00002431101,0.000005408855],"category_scores_gemma":[0.00007129805,0.0001138588,0.00005743909,0.001191891,0.0001872697,0.0005620749,0.0003948575,0.0001768462,0.000009214828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000802405,"about_ca_system_score_gemma":0.00004757128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000979685,"about_ca_topic_score_gemma":0.000006723718,"domain_scores_codex":[0.9982963,0.00002249307,0.0001679797,0.0005913503,0.000559966,0.0003619718],"domain_scores_gemma":[0.9994629,0.0001166656,0.0001182966,0.0002012086,0.00003482164,0.00006611506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002196979,0.0001363787,0.0000156129,0.00001595058,0.000009906536,7.157615e-7,0.001267248,0.02508662,0.1758522,0.06008478,0.00005503919,0.7374535],"study_design_scores_gemma":[0.0005675416,0.0005594391,0.0001514321,0.000001330523,0.000007029081,0.00007981571,0.001491142,0.9460027,0.03589499,0.008692346,0.006204035,0.0003482674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0131901,0.00001573548,0.9627945,0.00005934557,0.0005225469,0.0003906463,0.000001197205,0.0002792736,0.02274667],"genre_scores_gemma":[0.8006997,3.524726e-7,0.1985743,0.00009932462,0.0000530359,0.0004833121,0.000002546813,0.000007061742,0.00008038748],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.920916,"threshold_uncertainty_score":0.9381419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145022254","doi":"10.1109/tip.2004.823815","title":"On Missing Data Treatment for Degraded Video and Film Archives: A Survey and a New Bayesian Approach","year":2004,"lang":"en","type":"review","venue":"IEEE Transactions on Image Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Trinity College","funders":"Enterprise Ireland; University of Cambridge","keywords":"Computer science; Image restoration; Context (archaeology); Cultural heritage; Bayesian probability; Missing data; Noise (video); Computer vision; Multimedia; Artificial intelligence; Information retrieval; Image processing; Image (mathematics); Machine learning; Geography; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.1179582882245461,"gpt":0.3345613314993465,"spread":0.2166030432748004,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002402432,0.0005945177,0.0009366645,0.0004589622,0.0003851666,0.001256891,0.0006097298,0.0001630334,7.931276e-7],"category_scores_gemma":[0.00005729709,0.0004877714,0.0001505845,0.0005036416,0.0001639228,0.001161371,0.00001263978,0.0003153409,0.000002592134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001593513,"about_ca_system_score_gemma":0.0008354063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007632869,"about_ca_topic_score_gemma":0.00003630972,"domain_scores_codex":[0.9973761,0.0001247315,0.000482955,0.001372206,0.0002403228,0.0004036882],"domain_scores_gemma":[0.997792,0.0007335125,0.0002660049,0.0008789985,0.00003169333,0.0002977749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002717723,0.0001471041,3.677322e-8,0.004148834,0.00006076927,0.000004672975,0.000227361,0.00002085077,0.00000141814,0.00001025088,0.0000133203,0.9953382],"study_design_scores_gemma":[0.01297768,0.007388731,0.00001870927,0.1415752,0.004638821,0.004071339,0.0002175698,0.6531147,0.001197211,0.0159519,0.1506267,0.008221386],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[5.902926e-7,0.2632328,0.7352931,0.00003094832,0.0001634944,0.0008753791,0.0001469232,0.0001388611,0.0001179448],"genre_scores_gemma":[0.0005693014,0.4859214,0.5121237,0.00005599645,0.0001171333,0.0003733338,0.0001961752,0.0001603011,0.0004826942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9871168,"threshold_uncertainty_score":0.9997799,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2036651568","doi":"10.1080/13614576.2012.679446","title":"Digital Preservation, Archival Science and Methodological Foundations for Digital Libraries","year":2012,"lang":"en","type":"article","venue":"New Review of Information Networking","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Digital preservation; Digital curation; Computer science; Digital library; Data curation; World Wide Web; Work (physics); Key (lock); Data science; Library science; Engineering ethics; Engineering; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.07153507446322221,"gpt":0.3140304619340112,"spread":0.242495387470789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001133388,0.0001034666,0.000192248,0.0001319095,0.0001458979,0.000947102,0.0004217105,0.00002334361,0.000001698871],"category_scores_gemma":[0.003349074,0.00008528097,0.00005309762,0.0007861236,0.0001903223,0.03292506,0.0003012476,0.00006240933,0.000008320989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019673,"about_ca_system_score_gemma":0.0001880125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.457185e-7,"about_ca_topic_score_gemma":6.00155e-8,"domain_scores_codex":[0.9986556,0.0000171611,0.0005078739,0.0001155605,0.0004379651,0.0002658516],"domain_scores_gemma":[0.9984329,0.0005429874,0.0003328017,0.000247638,0.0002878492,0.0001557603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002735364,0.000006078904,0.0002443889,0.0003347685,0.000004045744,1.08373e-8,0.000188656,0.000006040416,0.000001565769,0.0978549,0.002035944,0.8993209],"study_design_scores_gemma":[0.0002766951,0.0001285143,0.003003903,0.001666314,0.0000136236,0.0000343768,0.00002748313,0.01283438,0.0001444553,0.02486654,0.956762,0.0002417604],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003101763,0.001889319,0.9681503,0.0007743573,0.0007800222,0.0005443604,0.000006337586,0.00009928277,0.02744587],"genre_scores_gemma":[0.388036,0.003069404,0.6031365,0.003622334,0.001484206,0.0001598025,0.0002153486,0.00002074066,0.0002556644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.954726,"threshold_uncertainty_score":0.9806009,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2999170936","doi":"10.1007/s11760-020-01636-0","title":"Image splicing detection using mask-RCNN","year":2020,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Discriminative model; Backbone network; Initialization; Feature (linguistics); Residual neural network; Pattern recognition (psychology); Pyramid (geometry); Convolutional neural network; Set (abstract data type); Feature vector; Computer vision; Network architecture; Mathematics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01990819478323003,"gpt":0.2486485119037041,"spread":0.2287403171204741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001921855,0.0001738212,0.000172169,0.00009212737,0.0002447215,0.001026168,0.0002149543,0.00005291544,0.000005477943],"category_scores_gemma":[0.0001122622,0.0001693056,0.00004450295,0.0005200697,0.00008980403,0.003489213,0.000193221,0.0001846932,0.00002387634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003728994,"about_ca_system_score_gemma":0.00006245545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002210596,"about_ca_topic_score_gemma":0.000002579667,"domain_scores_codex":[0.9987138,0.00003569333,0.0002335232,0.0004723227,0.0002502393,0.0002944539],"domain_scores_gemma":[0.9994142,0.00004131351,0.0001214304,0.0001298327,0.0001108983,0.0001823157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001114524,0.000007460048,0.00001980056,0.00009269601,0.00000426725,0.00002964553,0.0007349365,0.00001025583,0.5339035,0.00002056919,0.00001101985,0.4651547],"study_design_scores_gemma":[0.0002702263,0.0001240825,0.0001058597,0.0001067606,0.00001741912,0.00009128759,0.0001787857,0.6527023,0.344569,0.001385388,0.0001878396,0.0002610832],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1162457,0.0001602032,0.8816519,0.000370991,0.0001153414,0.0001061081,6.809834e-7,0.0002718089,0.001077349],"genre_scores_gemma":[0.9235547,0.000003005093,0.07558039,0.0005901056,0.0002368697,0.000004283708,6.255427e-7,0.00001864382,0.00001141908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.807309,"threshold_uncertainty_score":0.989536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3094138310","doi":"10.1016/j.cose.2020.102092","title":"A survey of machine learning techniques in adversarial image forensics","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Università degli Studi di Siena","keywords":"Computer science; Adversarial system; Computer security; Artificial intelligence; Adversarial machine learning; Data science; Computer vision; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01554365417211602,"gpt":0.2314940630839041,"spread":0.215950408911788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003458522,0.0001563967,0.0003008164,0.0001101827,0.00002955213,0.00008215025,0.0006241352,0.0000699846,0.000002156353],"category_scores_gemma":[0.0004060613,0.0001650348,0.00006768177,0.0007924165,0.00009958527,0.0005888739,0.0005565939,0.0003272812,0.000008726702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004409188,"about_ca_system_score_gemma":0.00005797538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005951004,"about_ca_topic_score_gemma":0.0002636125,"domain_scores_codex":[0.9985821,0.0001819298,0.0003352176,0.0003877084,0.0002771021,0.0002359882],"domain_scores_gemma":[0.9991223,0.0002114665,0.0001519716,0.0002644834,0.0001302016,0.0001195758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003276868,0.0003522358,0.0351668,0.0003137465,0.00008660831,0.0002622661,0.01593622,0.0004645617,0.002866055,0.008540396,0.004319779,0.9313636],"study_design_scores_gemma":[0.001453215,0.001140391,0.03168681,0.0001209575,0.000008656014,0.00001908437,0.00002990876,0.9185261,0.03568305,0.007219858,0.003484827,0.0006271066],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1127675,0.0001053078,0.8831993,0.0008464205,0.001053017,0.0003929837,0.00001921795,0.000593574,0.001022631],"genre_scores_gemma":[0.9573751,0.000008636591,0.04231156,0.0002046991,0.00006681441,0.000003622462,0.00001729027,0.00001075843,0.000001518729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9307365,"threshold_uncertainty_score":0.6729925,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147386143","doi":"10.1109/tifs.2009.2024715","title":"Digital image source coder forensics via intrinsic fingerprints","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Digital watermarking; Encoder; Source code; Watermark; Artificial intelligence; Image processing; Computer vision; Coding (social sciences); Fingerprint recognition; Fingerprint (computing); Image (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.004765004067404885,"gpt":0.1949385777863559,"spread":0.190173573718951,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001564414,0.0002676516,0.0002291626,0.0002858931,0.0002765566,0.0008913651,0.0002706635,0.0001402226,0.000007316828],"category_scores_gemma":[0.00002325739,0.0002636656,0.0001237261,0.0004614688,0.0001453586,0.006208815,0.000009030825,0.000355088,0.000152646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007947566,"about_ca_system_score_gemma":0.00004457304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008790739,"about_ca_topic_score_gemma":0.000009166758,"domain_scores_codex":[0.9984255,0.00001685367,0.000479163,0.0002676019,0.0004627894,0.0003480866],"domain_scores_gemma":[0.9988353,0.00007656953,0.0001688698,0.0004582278,0.000247882,0.0002132037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002804762,0.00004242472,0.00000314523,0.00001333182,0.0000159249,0.000002035935,0.001386255,0.0002793258,0.00001848981,0.004812534,0.0003620362,0.9930364],"study_design_scores_gemma":[0.004838444,0.002655209,0.001713005,0.0002089646,0.00009764924,0.0007395478,0.0005853184,0.5438828,0.04067599,0.3379746,0.06397186,0.002656604],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0195497,0.000006007699,0.9718235,0.0005299272,0.0009546636,0.000277396,0.0000355163,0.0003677462,0.006455531],"genre_scores_gemma":[0.9931415,0.00001823049,0.00578637,0.0009057665,0.00004056085,0.00001095428,0.00001648006,0.000009065018,0.00007112111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9903799,"threshold_uncertainty_score":0.9999816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965285802","doi":"10.1007/s10032-008-0076-2","title":"Low quality document image modeling and enhancement","year":2009,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Degradation (telecommunications); Shadow (psychology); Diffusion; Image restoration; Image enhancement; Computer vision; Artificial intelligence; Process (computing); Image (mathematics); Image processing; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01485540075618043,"gpt":0.2952721653155663,"spread":0.2804167645593859,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007732154,0.0001808504,0.0002531139,0.000639223,0.0001407484,0.001477492,0.0002993817,0.00004693684,0.0001197618],"category_scores_gemma":[0.00006815047,0.0001501522,0.000187455,0.0003065568,0.00003354307,0.00144688,0.00008654938,0.0001993057,0.00003508867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001567764,"about_ca_system_score_gemma":0.00002574236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002350124,"about_ca_topic_score_gemma":0.00001107997,"domain_scores_codex":[0.9977928,0.00008798148,0.0006131858,0.0003807801,0.0009246952,0.0002005347],"domain_scores_gemma":[0.9988834,0.00005075967,0.0003130138,0.0001563548,0.000400982,0.0001955423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008954907,0.0002220126,0.0001461723,0.000004626619,0.0009239389,0.00005156055,0.000331154,0.0004070768,0.001021793,0.002870098,0.0001535562,0.9937785],"study_design_scores_gemma":[0.006639283,0.002693911,0.009217881,0.0007083993,0.001064501,0.0005414179,0.0005042894,0.2417656,0.055567,0.676715,0.002506517,0.002076171],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5549504,0.00008447279,0.43853,0.00387128,0.0007529883,0.0001000593,0.000003804848,0.00004036312,0.001666627],"genre_scores_gemma":[0.9906313,0.0007871632,0.00715433,0.000962134,0.0002225096,0.000005918643,0.00002214489,0.000004579233,0.0002098656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9917023,"threshold_uncertainty_score":0.999559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972799026","doi":"10.3390/info10090286","title":"Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Discriminator; Computer science; Deep learning; Artificial intelligence; Convolutional neural network; Adversarial system; Image (mathematics); Convolution (computer science); Pattern recognition (psychology); Generative adversarial network; Artificial neural network; Computer vision; Detector; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.007330237573003794,"gpt":0.1956074419454287,"spread":0.1882772043724249,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002229982,0.0001138986,0.0001155712,0.00008642818,0.0001647514,0.0002843707,0.00006769851,0.0000857652,0.000003272782],"category_scores_gemma":[0.00004567208,0.0001154812,0.00002193993,0.0003291362,0.00004591256,0.005014265,0.00009909576,0.00008324793,0.00002135511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008113583,"about_ca_system_score_gemma":0.00003789615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002272862,"about_ca_topic_score_gemma":0.000009903239,"domain_scores_codex":[0.9991233,0.00003569917,0.000267075,0.0001483048,0.0002235943,0.0002020082],"domain_scores_gemma":[0.9994332,0.00006759815,0.0001878124,0.000126802,0.0001235042,0.00006106996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001451275,0.00001317361,0.01134575,0.00007249934,0.00004861743,0.000001181279,0.001911643,0.6440186,0.0003357787,0.0220731,0.0008859549,0.3191486],"study_design_scores_gemma":[0.000428551,0.00008595429,0.005180281,0.00002414407,0.000006617212,0.00004346682,0.00003253985,0.9882656,0.0003253423,0.003649241,0.001823819,0.000134415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2709792,0.00003660752,0.7260735,0.00002944249,0.002010787,0.0002970542,0.000002122313,0.00009763543,0.0004736403],"genre_scores_gemma":[0.9929797,0.000007456367,0.006293019,0.0003751068,0.0002975224,0.000008479882,0.00002060484,0.000004658734,0.00001342567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7220005,"threshold_uncertainty_score":0.4709187,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160197540","doi":"10.1109/tip.2013.2273672","title":"Automatic Inpainting Scheme for Video Text Detection and Removal","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Inpainting; Artificial intelligence; Computer science; Computer vision; Cluster analysis; Video compression picture types; Frame (networking); Motion compensation; Block-matching algorithm; Pattern recognition (psychology); Video tracking; Video processing; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01025403738276653,"gpt":0.232936739287884,"spread":0.2226827019051175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002083081,0.0001694116,0.0001483477,0.0002344091,0.0003887514,0.0008465723,0.0001826938,0.00005935698,0.000009787011],"category_scores_gemma":[0.00005796442,0.0001674408,0.00006160393,0.0004253785,0.00008533167,0.002992325,0.000003638274,0.000173595,0.00004970254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007111305,"about_ca_system_score_gemma":0.00004830236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001850815,"about_ca_topic_score_gemma":0.000009466954,"domain_scores_codex":[0.998804,0.00002327765,0.0002654512,0.0004024281,0.0002084674,0.000296355],"domain_scores_gemma":[0.9992481,0.0001311366,0.0001151083,0.0002224028,0.0001833825,0.00009981327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003668826,0.00003086832,5.720037e-7,0.0001083952,0.000006951374,0.000001434318,0.0002219821,0.00002619114,0.04820261,0.0000104375,0.00001225473,0.9513747],"study_design_scores_gemma":[0.0003628152,0.0001160464,0.00004745748,0.0001268952,0.00001164265,0.0001506387,0.00009147995,0.81691,0.1798311,0.001996887,0.0001539367,0.0002010821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08982323,0.00002913349,0.9081584,0.0002370327,0.000457477,0.0004138323,9.089963e-7,0.0005116575,0.0003683833],"genre_scores_gemma":[0.7516765,0.00000113571,0.2478675,0.0001367945,0.00003773747,0.0001732112,1.523431e-7,0.0000189007,0.00008803471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9511735,"threshold_uncertainty_score":0.8163517,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403221680","doi":"10.1145/3699710","title":"Deepfake Detection: A Comprehensive Survey from the Reliability Perspective","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Reliability (semiconductor); Perspective (graphical); Reliability engineering; Data science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.08163808677441459,"gpt":0.3430035862687424,"spread":0.2613654994943279,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00548955,0.0009342221,0.001845104,0.000230395,0.000392903,0.00127916,0.004404269,0.0004485314,0.000005713156],"category_scores_gemma":[0.006200738,0.0006340948,0.0008604973,0.00278316,0.0003089939,0.0003873974,0.003806026,0.001725457,0.001015204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008206086,"about_ca_system_score_gemma":0.0005212646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005339747,"about_ca_topic_score_gemma":0.001865431,"domain_scores_codex":[0.988151,0.007110626,0.001101493,0.002113539,0.0008303375,0.0006930117],"domain_scores_gemma":[0.9791607,0.01516795,0.0006167208,0.004033462,0.0008359951,0.0001852036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001625579,0.00003114861,0.00002367549,0.0005266226,0.00025723,0.00002751441,0.0003653961,0.00001250536,7.732352e-8,0.000161273,0.0009658752,0.9976271],"study_design_scores_gemma":[0.000367693,0.0003221054,0.01035216,0.009907155,0.0009155626,0.0003022783,0.000173512,0.007146644,0.000006726771,0.01757357,0.950528,0.002404554],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001392637,0.8972893,0.0894472,0.0001196632,0.01039837,0.001083365,0.0001744663,0.001021891,0.0003264215],"genre_scores_gemma":[0.008193801,0.9840586,0.004816932,0.0001830464,0.001983621,0.00009045839,0.0002702297,0.0002282538,0.0001750103],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9952225,"threshold_uncertainty_score":0.9997626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1990643530","doi":"10.1016/j.patcog.2008.10.021","title":"RSLDI: Restoration of single-sided low-quality document images","year":2008,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University","keywords":"Computer science; Artificial intelligence; Classifier (UML); Bayesian probability; Document image processing; Pixel; Pattern recognition (psychology); Image restoration; Machine learning; Data mining; Image (mathematics); Computer vision; Image processing; Image segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.05353682242814526,"gpt":0.2622833866411715,"spread":0.2087465642130262,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002401557,0.0001149384,0.0001522127,0.00013051,0.00006087919,0.00006035536,0.0001838965,0.00005363847,0.0000222994],"category_scores_gemma":[0.0002059384,0.0001179777,0.00006630127,0.0002363762,0.00007126552,0.001153532,0.00006841417,0.00007310934,0.0001816154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007912227,"about_ca_system_score_gemma":0.00002770097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001067393,"about_ca_topic_score_gemma":0.00002619503,"domain_scores_codex":[0.998668,0.0001031237,0.0003845801,0.0002886008,0.000389503,0.0001662455],"domain_scores_gemma":[0.9990447,0.0001175332,0.0002507452,0.0003109126,0.0002145189,0.00006157676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001581545,0.0001693842,0.001106851,0.00004881898,0.00001297264,0.00001550426,0.0005088731,0.00000667391,0.03090805,0.00002790061,0.0006026963,0.9665765],"study_design_scores_gemma":[0.0008432941,0.0004519655,0.03553011,0.0002104697,0.00001106698,0.00008723263,0.00004222381,0.0005185591,0.948552,0.01322332,0.0001661946,0.0003635866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7018253,0.00001452629,0.2950054,0.0001757845,0.000693249,0.0001718918,0.00001067878,0.0001377353,0.001965367],"genre_scores_gemma":[0.9953166,0.0000136388,0.00430729,0.0001412922,0.00009180867,0.00002463124,0.0000473913,0.000008626179,0.00004873493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9662129,"threshold_uncertainty_score":0.4810993,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377250587","doi":"10.1108/jfc-04-2022-0090","title":"The unethical use of deepfakes","year":2022,"lang":"en","type":"article","venue":"Journal of Financial Crime","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Context (archaeology); Originality; Compromise; Value (mathematics); Perspective (graphical); Commission; Public relations; Business; Psychology; Political science; Computer science; Social psychology; Law","retraction":null,"screen_n_in":null,"score":{"opus":0.02322430661042927,"gpt":0.2349342952144874,"spread":0.2117099886040582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006395908,0.00005350409,0.000126945,0.00007914959,0.0001654274,0.00007915032,0.0005686215,0.00002462228,0.000003748692],"category_scores_gemma":[0.001003047,0.00003786206,0.0001112351,0.0003011061,0.00007574422,0.0004646165,0.0001990532,0.0003559761,0.000002269657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005265552,"about_ca_system_score_gemma":0.0002378158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009986687,"about_ca_topic_score_gemma":0.000005104228,"domain_scores_codex":[0.998814,0.00008609762,0.0003507408,0.00007026265,0.0005497566,0.000129085],"domain_scores_gemma":[0.9989342,0.0003063882,0.0003458905,0.0001827409,0.00018246,0.00004832634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002537221,0.0001975555,0.0004224743,0.00001023408,0.00002641089,0.000178885,0.001111732,0.0007128795,0.003015802,0.2223323,0.02827972,0.7434583],"study_design_scores_gemma":[0.001114841,0.004350855,0.09017858,0.00005345015,0.00004004036,0.001514258,0.0001252148,0.003264763,0.02103723,0.10045,0.7775323,0.000338492],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8913156,0.0007135373,0.09160633,0.004023141,0.01060681,0.0001744701,0.00000642916,0.00003829827,0.001515428],"genre_scores_gemma":[0.9957644,0.00000988318,0.003714242,0.0002172034,0.0001608961,0.000001676251,9.376565e-8,0.000004104442,0.0001274537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7492526,"threshold_uncertainty_score":0.1546559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2979300174","doi":"10.3390/sym11101280","title":"Convolutional Neural Network for Copy-Move Forgery Detection","year":2019,"lang":"en","type":"article","venue":"Symmetry","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Set (abstract data type); Process (computing); Deep learning; Architecture; Limit (mathematics); Scheme (mathematics); Pattern recognition (psychology); Image (mathematics); Digital image; Data set; Machine learning; Computer vision; Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.00806733060437596,"gpt":0.2148070698731471,"spread":0.2067397392687712,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002328052,0.0001227034,0.0001472549,0.00009692134,0.00008039732,0.0001118692,0.0002798812,0.00008314403,0.000008829404],"category_scores_gemma":[0.00009839735,0.0001218599,0.0001169445,0.0004377069,0.00003216857,0.0006853889,0.00009785506,0.0001094026,0.0002343394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000831501,"about_ca_system_score_gemma":0.00003972487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008803983,"about_ca_topic_score_gemma":0.000007826437,"domain_scores_codex":[0.9988385,0.00002207065,0.0001902439,0.0003449853,0.0002455384,0.0003586554],"domain_scores_gemma":[0.9991405,0.0002544148,0.0000918605,0.0003349689,0.00009675098,0.0000815003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001193902,0.00008051714,0.008107759,0.00007447324,0.00007959559,0.000004841117,0.00007802384,0.001562761,0.002577201,0.138184,0.01443235,0.8346991],"study_design_scores_gemma":[0.002262398,0.001174322,0.04285621,0.00007519363,0.00002808931,0.0001664737,0.00005743614,0.7304171,0.02645164,0.1230549,0.07244758,0.00100868],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2953463,0.0001409406,0.6789261,0.0003129682,0.01680517,0.0007429584,0.00001193537,0.0004431207,0.0072706],"genre_scores_gemma":[0.9896624,9.30773e-7,0.008488633,0.0004235767,0.0005253456,0.00004028443,0.000008713245,0.00001392858,0.0008361571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8336904,"threshold_uncertainty_score":0.4969304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2921092418","doi":"10.1007/s11554-019-00866-x","title":"A multi-purpose image forensic method using densely connected convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pooling; Pattern recognition (psychology); JPEG; Robustness (evolution); Image (mathematics); Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.0149957316665437,"gpt":0.2802711769003,"spread":0.2652754452337562,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001022875,0.0003185393,0.0005890424,0.0004036349,0.000167762,0.0008473056,0.000688221,0.0001307602,0.00002868419],"category_scores_gemma":[0.0003929257,0.0002820033,0.0002445914,0.0008199695,0.0001580156,0.004600773,0.0002153262,0.0005168962,0.00003855684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002254242,"about_ca_system_score_gemma":0.000385325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001302649,"about_ca_topic_score_gemma":0.000001306794,"domain_scores_codex":[0.9972286,0.000196771,0.0008701359,0.0004233329,0.0007060071,0.0005751497],"domain_scores_gemma":[0.9964478,0.0003034136,0.001227088,0.0003297206,0.001422719,0.0002692759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002710021,0.0002367791,0.0003845608,0.0001864552,0.0001252222,0.0005554241,0.0006298696,0.009219846,0.6567606,0.00008407385,0.0007655232,0.3307806],"study_design_scores_gemma":[0.001392847,0.0002246942,0.000806792,0.0002818037,0.00005260443,0.003529376,0.0000527257,0.9847033,0.008052165,0.0005408361,0.00005264537,0.0003102004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.141423,0.0002585913,0.8564675,0.0001577956,0.0009117911,0.0002220872,0.000002113386,0.000120764,0.0004363839],"genre_scores_gemma":[0.2448699,0.00000787555,0.7545193,0.0001207351,0.0002817623,0.000001856046,0.000002258415,0.00003872647,0.0001576475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9754835,"threshold_uncertainty_score":0.9999632,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4288075323","doi":"10.18280/ts.390330","title":"Deepfakes Classification of Faces Using Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"King Khalid University","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Benchmark (surveying); Upload; Face (sociological concept); Transfer of learning; Pattern recognition (psychology); Generative model; Deep learning; Machine learning; Generative grammar","retraction":null,"screen_n_in":null,"score":{"opus":0.03237039336163008,"gpt":0.2338795054782328,"spread":0.2015091121166027,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488932,0.00008732059,0.0001029375,0.0001004625,0.0001574976,0.00004963237,0.0003438003,0.00001823706,0.00008755569],"category_scores_gemma":[0.000008172952,0.000092541,0.00005556094,0.0003237908,0.00006468282,0.0004099508,0.0001457606,0.00009903449,0.000001369783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001036135,"about_ca_system_score_gemma":0.00004015354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001246199,"about_ca_topic_score_gemma":0.000002955174,"domain_scores_codex":[0.9987993,0.00007606767,0.0002577114,0.0002197419,0.0004735408,0.0001736737],"domain_scores_gemma":[0.99953,0.00005842551,0.0001620776,0.0001458795,0.0000581411,0.00004544832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009939479,0.0004610259,0.006625926,0.00002787331,0.00008562563,0.0000130226,0.001035337,0.6698574,0.03706343,0.0712815,0.0009255856,0.2125239],"study_design_scores_gemma":[0.0002523944,0.0001740983,0.008453868,0.00000345681,0.000006783587,0.0000212121,0.00009590302,0.9891527,0.0006889797,0.0006474365,0.0004104411,0.00009274488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4696697,0.00005368099,0.5291063,0.0001363944,0.0005840193,0.0001439109,0.000006617663,0.00006329513,0.0002361666],"genre_scores_gemma":[0.9972162,5.231169e-7,0.002568444,0.00007635217,0.00007624616,0.00002746222,0.00001374277,0.000005635782,0.00001540384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5275465,"threshold_uncertainty_score":0.3773712,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3035170697","doi":"","title":"Semi-Supervised StyleGAN for Disentanglement Learning","year":2020,"lang":"en","type":"article","venue":"CaltechAUTHORS (California Institute of Technology)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Generalization; Generator (circuit theory); Controllability; Artificial intelligence; Representation (politics); Machine learning; External Data Representation; Feature learning; Unsupervised learning; Labeled data; Face (sociological concept); Identifiability; Semi-supervised learning; Mathematics; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01959542504845665,"gpt":0.2369606785372796,"spread":0.217365253488823,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002190633,0.0002741028,0.0004167928,0.0003816995,0.0001625112,0.00006541614,0.00120993,0.000273564,0.000006958603],"category_scores_gemma":[0.0009192341,0.0002680241,0.0001765191,0.00142046,0.0003149759,0.0005151269,0.0005434968,0.0003918145,0.00007612915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008479661,"about_ca_system_score_gemma":0.00009128438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004294409,"about_ca_topic_score_gemma":0.000003244769,"domain_scores_codex":[0.9979618,0.0000156306,0.0005522746,0.00065781,0.0003334276,0.000479082],"domain_scores_gemma":[0.9987826,0.00003155934,0.0002403522,0.0005409046,0.0001768089,0.000227791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000100475,0.0002036181,0.001116801,0.0004082491,0.0001446182,0.00005428865,0.0004252748,0.001111978,0.05406763,0.2601377,0.002268004,0.6799614],"study_design_scores_gemma":[0.001461887,0.001217724,0.00003108832,0.0001281262,0.00004633155,0.00002637486,0.0002116421,0.1227519,0.245,0.0164348,0.6120935,0.0005966107],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09262539,0.0001101439,0.8893177,0.0134847,0.001032892,0.0009003522,0.00006427911,0.001768639,0.0006958816],"genre_scores_gemma":[0.9002004,0.00001469066,0.09908636,0.0003241879,0.0001025642,0.0001630503,0.00003072121,0.00003066209,0.00004732177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.807575,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2922342173","doi":"10.1109/tip.2019.2904267","title":"Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Stony Brook University; Simon Fraser University; Government of Canada","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Context (archaeology); Adversarial system; RGB color model; Computer vision; Pattern recognition (psychology); Shadow (psychology); Artificial neural network; Image processing; Contextual image classification; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01891916739155991,"gpt":0.2269450545083561,"spread":0.2080258871167962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002618406,0.0003007649,0.0002943258,0.0002569423,0.0002697735,0.0005057828,0.0003815248,0.000135864,0.00002628291],"category_scores_gemma":[0.00004049801,0.0003240274,0.0001281128,0.0008382834,0.0001469383,0.003656704,0.00000959716,0.0004002318,0.00003914534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00030759,"about_ca_system_score_gemma":0.0001211824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003014266,"about_ca_topic_score_gemma":0.0003874122,"domain_scores_codex":[0.9977135,0.0001156081,0.0004506943,0.0006921294,0.0004566703,0.0005714207],"domain_scores_gemma":[0.9990278,0.0002232477,0.0001416182,0.0003223519,0.0001648504,0.0001201016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003606137,0.0004550635,0.0001546227,0.0001054239,0.00003440549,0.00001126596,0.0006857392,0.05522643,0.3741124,0.000009475547,0.00009370184,0.5687509],"study_design_scores_gemma":[0.002298909,0.0002662228,0.0003367459,0.0001748767,0.00002030572,0.00005937184,0.0001336309,0.8132848,0.1824491,0.000355166,0.0001808447,0.000440028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2362677,0.00009457099,0.7612194,0.00007796427,0.001489745,0.0004109863,0.00001877349,0.0002426023,0.0001781419],"genre_scores_gemma":[0.9824905,0.000001907642,0.01706726,0.0001168103,0.0001179027,0.00004578388,0.000004801238,0.00003591671,0.0001191465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7580584,"threshold_uncertainty_score":0.9999212,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963433197","doi":"","title":"A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes","year":2014,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Fields Institute for Research in Mathematical Sciences; University of Toronto","funders":"","keywords":"Deblurring; Regularization (linguistics); Computer science; Noise reduction; Exploit; Artificial intelligence; Pattern recognition (psychology); Image processing; A priori and a posteriori; Source code; Computer vision; Mathematics; Image restoration; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0110242690183514,"gpt":0.2054299133101598,"spread":0.1944056442918084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000192787,0.00005312111,0.00007749807,0.0001099031,0.00002789291,0.0001012651,0.0001478338,0.00002345554,3.764222e-7],"category_scores_gemma":[0.0001320508,0.00004732895,0.00001196318,0.0002660782,0.00002258222,0.000396083,0.0001263945,0.00002456977,0.000003326355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008929849,"about_ca_system_score_gemma":0.000007662897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008834932,"about_ca_topic_score_gemma":0.000003043333,"domain_scores_codex":[0.999442,0.00001754457,0.0001111662,0.0001890619,0.0001433926,0.000096875],"domain_scores_gemma":[0.9996263,0.00003562508,0.00003116686,0.0001997627,0.00004481272,0.00006232628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008660294,0.0000457642,0.0005134721,0.00004285968,0.000007980083,2.378147e-7,0.0009910095,0.0008475071,0.01437435,0.3296776,0.00005773644,0.6534328],"study_design_scores_gemma":[0.0007338811,0.0002949955,0.004983031,0.00008425869,0.000009240031,0.00004472359,0.0001001081,0.6653921,0.2900396,0.03711611,0.0008685541,0.0003333731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442022,0.000004538915,0.8347576,0.00005468212,0.00007294168,0.00007634563,6.345121e-8,0.00005545635,0.02077615],"genre_scores_gemma":[0.8246552,2.41275e-7,0.1751203,0.00006012271,0.00001880885,0.000002796886,3.330454e-7,0.000003500932,0.000138676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6804531,"threshold_uncertainty_score":0.1930019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888516611","doi":"10.1109/access.2018.2866089","title":"Sparsity-Based Image Inpainting Detection via Canonical Correlation Analysis With Low-Rank Constraints","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Inpainting; Artificial intelligence; Computer science; Pattern recognition (psychology); Image (mathematics); JPEG; Feature (linguistics); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009953577584668239,"gpt":0.2505138867084685,"spread":0.2405603091238003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003295079,0.000163508,0.0002111601,0.000414485,0.0001877525,0.0005627437,0.0005885776,0.00008649394,0.0000258872],"category_scores_gemma":[0.0000913328,0.0001495734,0.00008840017,0.002154771,0.0003813821,0.00182596,0.00008129895,0.0001665302,0.00008405292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157049,"about_ca_system_score_gemma":0.00009675425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000247283,"about_ca_topic_score_gemma":0.002648022,"domain_scores_codex":[0.998458,0.00007425645,0.0002569105,0.0004891488,0.0004154576,0.0003061741],"domain_scores_gemma":[0.9987229,0.0001327421,0.0002108757,0.0004841205,0.0003265422,0.0001228791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003354237,0.0003975026,0.06895056,0.00008969891,0.0008293115,0.0001677914,0.0007880144,0.01011146,0.02804821,0.0003242542,0.0002248716,0.8897329],"study_design_scores_gemma":[0.0008292705,0.0003360981,0.04898901,0.00005077457,0.0001924512,0.00003259793,0.00001010611,0.6955759,0.2529291,0.0005459942,0.00009506856,0.000413645],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3736274,8.724523e-7,0.6242182,0.00005450076,0.0006412941,0.0001150197,0.000001323871,0.0001720841,0.001169323],"genre_scores_gemma":[0.9907155,1.608238e-7,0.008876362,0.0001928058,0.000168654,0.00001248064,0.000004703023,0.00001095615,0.00001835715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8893192,"threshold_uncertainty_score":0.6099424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285176942","doi":"10.1109/access.2022.3179116","title":"Forensic Analysis of Synthetically Generated Western Blot Images","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Air Force Research Laboratory; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Nvidia","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Computational biology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.02164088992676621,"gpt":0.2752600669628085,"spread":0.2536191770360423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000206258,0.0001088312,0.0002571672,0.0004730979,0.00008764207,0.0002149492,0.001295089,0.00002182003,0.00004645239],"category_scores_gemma":[0.00004184798,0.0001061766,0.0001288653,0.002542294,0.00007459605,0.0006790436,0.0005203715,0.0001038445,0.00000876736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000456001,"about_ca_system_score_gemma":0.00004512433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007347722,"about_ca_topic_score_gemma":0.00005171,"domain_scores_codex":[0.9986095,0.00007178329,0.0002719095,0.0003447796,0.0004925783,0.0002094329],"domain_scores_gemma":[0.9989776,0.0001005414,0.0001441706,0.0005882781,0.0001210242,0.00006836341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001168905,0.0009721603,0.03338439,0.0000721513,0.003164487,0.0002971498,0.001485123,0.1112122,0.04508481,0.006999466,0.00693298,0.7902782],"study_design_scores_gemma":[0.0008554179,0.0006746851,0.05795346,0.0000215925,0.001022262,0.00009785158,0.0000654117,0.2915394,0.6389855,0.005444441,0.002401517,0.0009385224],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8279343,0.00002860967,0.169044,0.000261353,0.0012226,0.0001146059,0.00002613219,0.0001456036,0.001222832],"genre_scores_gemma":[0.9983468,0.000001574914,0.001177611,0.0002104525,0.00003071718,0.0000354613,0.000006526985,0.000009258332,0.0001815651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7893397,"threshold_uncertainty_score":0.4329756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963502975","doi":"10.1109/cvpr.2019.00439","title":"Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Inpainting; Computer science; Artificial intelligence; Encoder; Pixel; Computer vision; Focus (optics); Residual; Frame (networking); Deep learning; Video decoder; Coherence (philosophical gambling strategy); Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01602982401692345,"gpt":0.2442405746136241,"spread":0.2282107505967007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284325,0.0002330603,0.0002240148,0.0001638048,0.00005811747,0.0008137894,0.0005461,0.0002241852,0.000009302172],"category_scores_gemma":[0.0001494727,0.0002281486,0.00005429914,0.0001982489,0.00006544335,0.0008894466,0.001085387,0.0003724965,0.0001134044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008010082,"about_ca_system_score_gemma":0.00009462643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001126969,"about_ca_topic_score_gemma":0.00004657724,"domain_scores_codex":[0.9982664,0.00004395195,0.0003029208,0.0007838313,0.0003770125,0.0002259108],"domain_scores_gemma":[0.9987523,0.0001007231,0.0002503825,0.0006551672,0.0001269737,0.000114484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001028367,0.00002862648,0.001524136,0.00008224593,0.00002128982,0.000002955946,0.0003521396,0.0004154807,0.00006794951,0.002206625,0.002675185,0.9926131],"study_design_scores_gemma":[0.0008230895,0.0002129781,0.0007024912,0.0005565287,0.0000210253,0.00005879166,0.00004263043,0.9333738,0.00877166,0.04907526,0.005381847,0.0009798832],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04544513,0.0004954598,0.9429123,0.0003524399,0.00324289,0.0004495157,0.000002881294,0.000282122,0.006817282],"genre_scores_gemma":[0.934707,0.00005660314,0.06398348,0.0001297351,0.00009459921,0.00004197257,0.00005626331,0.00001556994,0.0009147798],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9916332,"threshold_uncertainty_score":0.9303629,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3013074498","doi":"10.1109/jiot.2020.2983213","title":"Smart Meter Data Obfuscation Using Correlated Noise","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obfuscation; Computer science; Noise (video); Smart meter; Noise measurement; Data mining; Big data; Artificial intelligence; Noise reduction; Smart grid; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.07415349170297292,"gpt":0.2719266096692166,"spread":0.1977731179662437,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003863578,0.0001163678,0.0001764057,0.0001109845,0.0000332786,0.0003206196,0.001643925,0.00005829934,0.00001584594],"category_scores_gemma":[0.000319391,0.0001049947,0.00006983893,0.0002554864,0.00004721504,0.003309001,0.0003745096,0.0003576436,0.00004725198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005554357,"about_ca_system_score_gemma":0.0000670165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004955829,"about_ca_topic_score_gemma":0.00000115368,"domain_scores_codex":[0.9986518,0.00005048044,0.0004374731,0.0002683136,0.0004182748,0.000173653],"domain_scores_gemma":[0.998787,0.00005453896,0.000424289,0.0003944034,0.0001713012,0.0001684134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004069365,0.0003684426,0.003551038,0.0001660414,0.0008126051,0.0004297161,0.03024563,0.002317901,0.3059623,0.001469133,0.06878319,0.5854871],"study_design_scores_gemma":[0.0003644476,0.0002068512,0.0001460002,0.0001379421,0.00002911657,0.0006494697,0.00002954868,0.9323777,0.0635429,0.0007930971,0.001572738,0.0001502111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2454653,0.00004321324,0.74953,0.0007998153,0.003418262,0.00006025095,0.000001782516,0.00006409403,0.0006172966],"genre_scores_gemma":[0.9603913,0.000004497038,0.03864802,0.0006958406,0.0001793509,3.331272e-7,0.000002690587,0.00001173597,0.00006621685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9300598,"threshold_uncertainty_score":0.4281561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388023184","doi":"10.18280/ts.400521","title":"Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Digital forensics; Identification (biology); Expediting; Field (mathematics); Machine learning; Object (grammar); Pattern recognition (psychology); Computer vision; Engineering; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.02477361431543234,"gpt":0.2399835304050656,"spread":0.2152099160896332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005182156,0.0001254248,0.0001691204,0.0004795727,0.00005374318,0.00008215186,0.0001232079,0.00004955783,0.000004445959],"category_scores_gemma":[0.00004479905,0.0001383139,0.00004313509,0.0008885185,0.00005788028,0.001421028,0.00006607526,0.00006802125,0.00001929786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060794,"about_ca_system_score_gemma":0.00003202461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005603205,"about_ca_topic_score_gemma":0.00003703614,"domain_scores_codex":[0.9986135,0.0000620611,0.0004322006,0.0003602655,0.0003045069,0.0002275158],"domain_scores_gemma":[0.9993734,0.00009928494,0.0001977149,0.0001768762,0.0001039539,0.0000487812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004152272,0.00003680176,0.00002519724,0.0000358951,0.000009597864,0.000008630886,0.0005056363,0.00676645,0.6491936,0.0001540573,0.000008419433,0.3432141],"study_design_scores_gemma":[0.0005312029,0.0001447549,0.004347489,0.0000698059,0.00000973676,0.00001447178,0.00008388431,0.7885412,0.1885969,0.01751444,0.00000149997,0.0001446495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293962,0.00000845766,0.06952231,0.0000275947,0.000245179,0.0003383851,0.000006722649,0.000187175,0.0002679684],"genre_scores_gemma":[0.9969585,0.00002386487,0.002895829,0.000007601895,0.000036375,0.00003110496,0.00001617827,0.00001249769,0.00001803555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7817748,"threshold_uncertainty_score":0.5640277,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3136555065","doi":"10.1016/j.patrec.2021.03.009","title":"Perception matters: Exploring imperceptible and transferable anti-forensics for GAN-generated fake face imagery detection","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Adversarial system; Perception; Face (sociological concept); Face detection; Deep learning; Benchmark (surveying); Computer vision; Facial recognition system; Machine learning; Pattern recognition (psychology); Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04359750057162896,"gpt":0.2272622417478511,"spread":0.1836647411762222,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002183949,0.0002701785,0.0002534742,0.0002188787,0.0002226626,0.0005835064,0.0001490813,0.0000889907,0.000034193],"category_scores_gemma":[0.00004364923,0.000305914,0.0001286431,0.0004007398,0.00006736638,0.00218658,0.00004719999,0.0001869619,0.00009682677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071454,"about_ca_system_score_gemma":0.00002364379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003508376,"about_ca_topic_score_gemma":0.00003984912,"domain_scores_codex":[0.9981061,0.00009075977,0.0003561961,0.0007241023,0.0002752011,0.0004477041],"domain_scores_gemma":[0.9991571,0.0001071486,0.00008682316,0.0003070467,0.0002074281,0.0001344015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007067992,0.00002356395,0.0000938593,0.00006872418,0.00002147526,0.00001527557,0.0006038109,0.00004909032,0.4335025,0.000001063976,0.0003438888,0.5652697],"study_design_scores_gemma":[0.00189935,0.0002396599,0.006371282,0.0002398275,0.0000784966,0.0004299522,0.001251102,0.02770867,0.9588143,0.0006198819,0.001332169,0.001015357],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4938145,0.000009893711,0.502381,0.002343709,0.0009891751,0.0002218586,0.0000268224,0.0001871128,0.00002598017],"genre_scores_gemma":[0.9826657,0.00008753674,0.01007973,0.006385146,0.0002925771,0.0002522874,0.0001677826,0.00004522728,0.00002399463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5642543,"threshold_uncertainty_score":0.9999393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2802173845","doi":"10.1109/icassp.2018.8462057","title":"A Rotation-Invariant Convolutional Neural Network for Image Enhancement Forensics","year":2018,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Convolutional neural network; Computer science; Artificial intelligence; Robustness (evolution); Invariant (physics); Pattern recognition (psychology); Rotation (mathematics); Computer vision; Artificial neural network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01451261910649984,"gpt":0.2429311390566931,"spread":0.2284185199501932,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001959801,0.0001104238,0.0001024157,0.00003926389,0.0001497709,0.0001804871,0.0002798705,0.00003416594,0.00003538578],"category_scores_gemma":[0.00009357204,0.0000984879,0.00006096575,0.0002471699,0.000133309,0.0007004368,0.0001076565,0.00004127617,0.0001396382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005388685,"about_ca_system_score_gemma":0.00006935306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000011779,"about_ca_topic_score_gemma":0.00005370967,"domain_scores_codex":[0.9988958,0.00001476342,0.0002107168,0.0003058146,0.0002398336,0.0003330209],"domain_scores_gemma":[0.999116,0.0001230252,0.00007490467,0.0002805924,0.0003202099,0.00008524615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004454797,0.00004895685,0.00005225579,0.00001195953,0.00003605143,0.000003842353,0.0002247787,0.0001283173,0.001333305,0.7761331,0.07292482,0.1490581],"study_design_scores_gemma":[0.0007985875,0.0008873311,0.0006942769,0.00001692853,0.000009506085,0.00003289174,0.00002158574,0.8048071,0.01866902,0.160578,0.01319441,0.0002903375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004415651,0.000008418547,0.9836443,0.000968635,0.002702898,0.0003872721,0.000003678049,0.0001641922,0.007704928],"genre_scores_gemma":[0.4265385,6.395135e-7,0.5704231,0.001049963,0.0009708478,0.000109231,0.00001432374,0.000009245014,0.0008841702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8046787,"threshold_uncertainty_score":0.401622,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4299806750","doi":"10.1109/icme52920.2022.9859621","title":"Mel-Spectrogram Image-Based End-to-End Audio Deepfake Detection Under Channel-Mismatched Conditions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Multimedia and Expo (ICME)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spectrogram; Codec; Robustness (evolution); Speech recognition; Spoofing attack; Speech coding; Jitter; Artificial intelligence; Computer network; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.03054497750112798,"gpt":0.2803384128655858,"spread":0.2497934353644578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003512594,0.0003185154,0.0002628689,0.000593159,0.0004144254,0.0004525089,0.0008353377,0.00008014173,0.0009681969],"category_scores_gemma":[0.000116479,0.0003477023,0.0001343405,0.0005128428,0.0001503545,0.0006242229,0.0002796377,0.0005046245,0.0002154996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000379543,"about_ca_system_score_gemma":0.0001595236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001085458,"about_ca_topic_score_gemma":0.0001712744,"domain_scores_codex":[0.9970674,0.0001389021,0.0004029442,0.0008234914,0.001122107,0.0004451682],"domain_scores_gemma":[0.9985666,0.0002399652,0.0001951869,0.0004349027,0.0002554829,0.0003078115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008895725,0.001943498,0.0001814072,0.00005593919,0.0005737035,0.0003983148,0.007051808,0.008986235,0.4383278,0.06764644,0.007835049,0.4661102],"study_design_scores_gemma":[0.002579371,0.001548784,0.001823567,0.00005560038,0.00003654623,0.0001632667,0.001669053,0.8502179,0.1150673,0.01967945,0.006188294,0.000970853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3805448,0.00004209,0.5746018,0.01271857,0.01745353,0.001475587,0.0005067455,0.001062477,0.01159449],"genre_scores_gemma":[0.9938688,0.00001199583,0.003382162,0.001162025,0.0002576289,0.0005943764,0.0001691177,0.00002919544,0.0005247148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8412316,"threshold_uncertainty_score":0.999945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100124582","doi":"10.1109/icassp.2009.4959883","title":"Feature based classification of computer graphics and real images","year":2009,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Histogram; Feature (linguistics); Artificial intelligence; Computer graphics; Feature selection; Graphics; Contextual image classification; Pattern recognition (psychology); Histogram of oriented gradients; Feature extraction; Computer vision; Image (mathematics); Computer graphics (images)","retraction":null,"screen_n_in":null,"score":{"opus":0.01078333483164279,"gpt":0.2251490207379073,"spread":0.2143656859062645,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007828794,0.00005812358,0.00007276452,0.00008776108,0.00001937791,0.00006429038,0.0001427007,0.00004069513,6.025649e-7],"category_scores_gemma":[0.000009669121,0.00004805865,0.0000224274,0.000232905,0.00004430783,0.0003343169,0.00002080787,0.0000469626,0.000001766962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006519724,"about_ca_system_score_gemma":0.00001437912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005801371,"about_ca_topic_score_gemma":0.000003814236,"domain_scores_codex":[0.9995276,0.0000135134,0.00007417615,0.000165314,0.0001404732,0.00007887211],"domain_scores_gemma":[0.9995847,0.00003830277,0.00004738005,0.0002220548,0.00006634695,0.00004124689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000007494275,0.00006094858,0.0008234807,0.00001123505,0.000004347158,0.000002760184,0.00006528643,0.000006611209,0.005533061,0.2719566,0.007945063,0.7135832],"study_design_scores_gemma":[0.0004776653,0.0006021587,0.648205,0.00002778718,0.000006024397,0.00001621127,0.000007329912,0.3066453,0.02688603,0.01553673,0.001392534,0.000197248],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03346546,0.0000136515,0.9531352,0.003860645,0.0002252852,0.00009684637,0.000001174197,0.0001663988,0.009035329],"genre_scores_gemma":[0.9302351,0.00000570354,0.06934465,0.0003061738,0.00002718327,8.794095e-7,0.000001970161,0.000001797777,0.00007646852],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8967697,"threshold_uncertainty_score":0.1959775,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140470795","doi":"10.1109/icassp.2007.365987","title":"Block Size Forensic Analysis in Digital Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Block (permutation group theory); Computer science; Block size; Artificial intelligence; Context (archaeology); Pattern recognition (psychology); False alarm; Digital image; Computer vision; Image processing; Digital forensics; Image (mathematics); Mathematics; Key (lock); Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.005319055626112228,"gpt":0.217362042221617,"spread":0.2120429865955047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002323634,0.0001035686,0.0001579093,0.0003713784,0.00001954286,0.0002927719,0.0003395981,0.00003875461,0.00001001203],"category_scores_gemma":[0.0002646366,0.00009033724,0.0001061815,0.002238438,0.0000481652,0.001206633,0.0001480259,0.00007399303,0.00009861851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005600174,"about_ca_system_score_gemma":0.00001885049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003824381,"about_ca_topic_score_gemma":0.0004099284,"domain_scores_codex":[0.9988806,0.000004125147,0.0002368315,0.000304218,0.0002665971,0.0003075963],"domain_scores_gemma":[0.9991441,0.0002772685,0.00004375175,0.000381741,0.00005766144,0.00009545573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001713665,0.0001729539,0.04603073,0.000006407514,0.0001750829,0.0002250596,0.0003436965,0.0002609148,0.0004486217,0.0143677,0.001311409,0.9366403],"study_design_scores_gemma":[0.001897516,0.0005369131,0.7740798,0.00003264233,0.0001305269,0.0001513472,0.0004089134,0.03731961,0.1163303,0.06291938,0.004628098,0.001565027],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5119559,0.00001431898,0.3526986,0.0002128697,0.000381854,0.0001036529,0.000001630956,0.0002970421,0.1343341],"genre_scores_gemma":[0.986596,5.342224e-7,0.01158097,0.0001219529,0.00003178918,0.000001833287,0.000001073176,0.000004692818,0.001661164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9350753,"threshold_uncertainty_score":0.3683846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3174762625","doi":"10.1007/s11042-021-11126-1","title":"Towards general object-based video forgery detection via dual-stream networks and depth information embedding","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Tianjin Science and Technology Program; Natural Science Foundation of Tianjin City","keywords":"Computer science; Conditional random field; Artificial intelligence; Discriminative model; Focus (optics); Video tracking; Segmentation; Feature (linguistics); Embedding; Codec; Object (grammar); Pattern recognition (psychology); Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.01070534111153067,"gpt":0.2331004745540335,"spread":0.2223951334425028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001447824,0.0001534496,0.0001498695,0.0001038423,0.0002102037,0.0005441983,0.0001014755,0.0001118107,0.000003169912],"category_scores_gemma":[0.0001051351,0.0001560766,0.00004575555,0.0004614196,0.00006843547,0.001469965,0.000120797,0.0001433531,0.00001424285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004372609,"about_ca_system_score_gemma":0.0000628501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003093599,"about_ca_topic_score_gemma":0.00006006608,"domain_scores_codex":[0.9989594,0.00002860581,0.0002811382,0.0003086486,0.0001881106,0.0002340901],"domain_scores_gemma":[0.9990893,0.0001702411,0.0001141572,0.0003227889,0.000145338,0.0001581633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003062821,0.0000224381,0.0001047282,0.00001392349,0.000008708766,0.000001356515,0.00009136127,0.0009485306,0.001141079,0.0004417842,0.00005988601,0.9971631],"study_design_scores_gemma":[0.0004423423,0.00004052542,0.01071909,0.00001625283,0.00001638633,0.00005725567,0.00004374517,0.9566842,0.02159169,0.0008618468,0.009296616,0.0002300814],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02412481,0.0001132844,0.973763,0.0002421119,0.000279405,0.0003629776,0.00001449596,0.0002043226,0.0008955552],"genre_scores_gemma":[0.9437976,0.00004741923,0.05498027,0.0003237499,0.0002322624,0.0004555596,0.000121222,0.0000104852,0.00003138212],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.996933,"threshold_uncertainty_score":0.6364621,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224918448","doi":"10.1109/icassp43922.2022.9746888","title":"ADT: Anti-Deepfake Transformer","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; University of Science and Technology of China","keywords":"Leverage (statistics); Computer science; Convolutional neural network; Security token; Residual; Transformer; Artificial intelligence; Mainstream; Deep learning; Labeled data; Machine learning; Data mining; Pattern recognition (psychology); Computer security; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.03506727168756474,"gpt":0.2771348645164651,"spread":0.2420675928289003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006918729,0.0004342813,0.0004109841,0.0005225232,0.0006584916,0.0009832943,0.001502663,0.0001180092,0.001138346],"category_scores_gemma":[0.0001099377,0.0004502082,0.0001468827,0.0006374582,0.0002293328,0.001037123,0.000325574,0.001061462,0.0000777733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003208162,"about_ca_system_score_gemma":0.0004078752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002973872,"about_ca_topic_score_gemma":0.000009303727,"domain_scores_codex":[0.9954823,0.0001237651,0.0006517106,0.001063703,0.002060441,0.000618084],"domain_scores_gemma":[0.9983808,0.0001657755,0.0003404952,0.0003971276,0.0004145305,0.0003013197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002599053,0.0007300153,0.0001874127,0.00009952141,0.000158668,0.0006751751,0.00107736,0.001868341,0.09899914,0.01712717,0.007217908,0.8715994],"study_design_scores_gemma":[0.002048203,0.001667064,0.0006893545,0.0001862377,0.00008172994,0.001041374,0.001380407,0.9305632,0.01262432,0.02945419,0.01877038,0.001493576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1794648,0.0004529729,0.7314556,0.01059254,0.0108496,0.001199806,0.0006595547,0.001107493,0.06421759],"genre_scores_gemma":[0.9920694,0.00008624867,0.003949774,0.001068957,0.0003798898,0.0001084148,0.00006313123,0.00004437229,0.002229784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9286948,"threshold_uncertainty_score":0.999795,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W42110184","doi":"10.1007/978-3-642-24434-6_1","title":"Finding Homoglyphs - A Step towards Detecting Unicode-Based Visual Spoofing Attacks","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Unicode; Computer science; Spoofing attack; Artificial intelligence; Programming language; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.02659705472081776,"gpt":0.2590963164364769,"spread":0.2324992617156592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001367286,0.0008572772,0.0007589422,0.001778391,0.0004133022,0.00116774,0.003652195,0.0005212863,0.00002562194],"category_scores_gemma":[0.0003769187,0.0008433912,0.0002650241,0.001283417,0.0008090944,0.001380224,0.00180935,0.001361179,0.00009709353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008142518,"about_ca_system_score_gemma":0.001184157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008621469,"about_ca_topic_score_gemma":0.0001721099,"domain_scores_codex":[0.9941355,0.00005491278,0.0008055517,0.00223255,0.001561098,0.001210383],"domain_scores_gemma":[0.9965754,0.0005988075,0.0005640018,0.00158474,0.0003389109,0.0003381839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001081068,0.0000282475,0.00003455662,0.00004349844,0.00001194312,0.0001545667,0.000531819,0.004676156,0.0001364562,0.002532366,0.000004776892,0.9918348],"study_design_scores_gemma":[0.0007493966,0.0008950572,0.0001463888,0.001553584,0.0000267902,0.0002391319,8.497348e-7,0.9237584,0.01962312,0.04961106,0.001490655,0.001905583],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001428297,0.0001335749,0.9837185,0.000119427,0.004906229,0.0004929536,0.000004362678,0.0005333336,0.008663299],"genre_scores_gemma":[0.6141231,0.000007027918,0.3840825,0.0008198282,0.0005645205,0.00001890447,0.000005240197,0.00009059015,0.000288304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9899292,"threshold_uncertainty_score":0.9998692,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3091508658","doi":"10.1109/ijcnn48605.2020.9207309","title":"Cross-representation transferability of adversarial attacks: From spectrograms to audio waveforms","year":2020,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; McGill University","funders":"","keywords":"Spectrogram; Computer science; Transferability; Speech recognition; Convolutional neural network; Waveform; Artificial intelligence; Adversarial system; Fourier transform; Pattern recognition (psychology); Mathematics; Machine learning; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01899236776660037,"gpt":0.2721492946560581,"spread":0.2531569268894577,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000156601,0.0001821979,0.0002935247,0.00006318188,0.00005317239,0.0002223785,0.000622267,0.00008279066,0.00005894961],"category_scores_gemma":[0.0003460734,0.0001680333,0.0001547323,0.0007748331,0.000100403,0.001059636,0.00017387,0.0001541255,0.0002152513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008720023,"about_ca_system_score_gemma":0.0000644798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002982701,"about_ca_topic_score_gemma":0.0001642993,"domain_scores_codex":[0.9981197,0.00004917327,0.0003373396,0.0006314439,0.0005575341,0.0003048475],"domain_scores_gemma":[0.9988112,0.0001164552,0.00008879176,0.0005733635,0.0001188753,0.0002913578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001903367,0.0006203762,0.03386505,0.0001322081,0.0003109112,0.00007899303,0.05348637,0.006857268,0.07744204,0.009719482,0.003079598,0.8125044],"study_design_scores_gemma":[0.005046999,0.002812013,0.21324,0.00009166032,0.00006480147,0.00001220553,0.0007715374,0.04122338,0.707615,0.01042361,0.01744599,0.001252755],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431614,0.000009345932,0.2492177,0.004263043,0.001109061,0.0004072865,0.00002428865,0.0002239229,0.001583964],"genre_scores_gemma":[0.9891341,0.000001789115,0.01010848,0.0003011545,0.0003123124,0.00001731229,0.0000139925,0.00001421085,0.0000966778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8112516,"threshold_uncertainty_score":0.6852199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4220709567","doi":"10.1109/icais53314.2022.9742798","title":"Analysis of Deep Learning Methods for Detection of Bird Species","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.0627383028276587,"gpt":0.318791137053182,"spread":0.2560528342255233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006292387,0.0001433098,0.0003159241,0.0008418676,0.0001661132,0.00009661124,0.0005584573,0.00004617159,0.0009072658],"category_scores_gemma":[0.0002453167,0.0001514644,0.0001970818,0.0009117691,0.0001290746,0.0003205708,0.0002661833,0.000174771,0.000002213606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006480035,"about_ca_system_score_gemma":0.00004220102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001041588,"about_ca_topic_score_gemma":0.0005455045,"domain_scores_codex":[0.9982999,0.0001606062,0.0005616483,0.0004213803,0.0003837226,0.0001727401],"domain_scores_gemma":[0.9986162,0.0003892719,0.0003504583,0.000234297,0.0003489067,0.00006090691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007259446,0.00006832184,0.00006556638,0.000004718304,0.0002126686,6.694301e-7,0.0003793691,0.003200887,0.03471611,0.4137897,0.000003709343,0.5474856],"study_design_scores_gemma":[0.000038028,0.0006248343,0.0003213367,0.000005158605,0.00004844373,0.000003106345,0.0008541351,0.7298414,0.2314198,0.02979343,0.006901709,0.0001485903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03737208,0.0000497393,0.9559869,0.0001955493,0.001332126,0.00009319239,0.0000239437,0.00005570385,0.004890732],"genre_scores_gemma":[0.9957823,0.00004611138,0.003350657,0.00009172015,0.00004314847,0.00007873441,0.00002346505,0.00001061206,0.0005732597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9584102,"threshold_uncertainty_score":0.9933923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405843673","doi":"10.1038/s41598-024-82223-y","title":"Enhancing practicality and efficiency of deepfake detection","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Computer science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.008676008576843905,"gpt":0.2437657504445885,"spread":0.2350897418677446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001814918,0.00008199563,0.0001057345,0.0002194154,0.0001076525,0.0007319421,0.0001093206,0.00004172248,0.000004246118],"category_scores_gemma":[0.0006246572,0.00007157194,0.00004664854,0.0009677877,0.0002086565,0.001082151,0.000135605,0.0000939012,0.00001336628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003779444,"about_ca_system_score_gemma":0.000109183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001393901,"about_ca_topic_score_gemma":0.00002900972,"domain_scores_codex":[0.9982733,0.00002986726,0.0003712861,0.0006688319,0.000474247,0.0001825306],"domain_scores_gemma":[0.9990087,0.0001054059,0.0001229768,0.0005655605,0.0001127103,0.00008466761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003188064,0.00006992042,0.0001386072,0.0002103351,0.00001658518,0.0004081835,0.001477935,0.00001682473,0.431284,0.005383416,0.0002483602,0.5607426],"study_design_scores_gemma":[0.00002835156,0.0000706367,0.0003480376,0.0000882919,0.00001020859,0.001123412,0.00004312774,0.01652797,0.9297293,0.04296371,0.008940148,0.0001267708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5200884,0.0002152023,0.4609895,0.00008348012,0.0161857,0.0001415733,2.369248e-7,0.0002334778,0.002062496],"genre_scores_gemma":[0.9964355,0.00000131867,0.003214412,0.000005149022,0.00003319687,0.000006154852,7.068383e-7,0.000004945879,0.000298645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5606158,"threshold_uncertainty_score":0.7058134,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2062233254","doi":"10.1109/icip.2011.6115853","title":"Modeling the EXIF-Image correlation for image manipulation detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Feature (linguistics); Noise (video); Image (mathematics); Computer vision; Feature selection; Feature detection (computer vision); Correlation; Image file formats; Header; Statistical model; Image editing; Feature extraction; Image processing; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04354613269985617,"gpt":0.2332412253540317,"spread":0.1896950926541755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002756726,0.00009585135,0.00006765971,0.00007971846,0.0001554917,0.0001616277,0.0002567781,0.00004959853,0.000009327079],"category_scores_gemma":[0.0001011871,0.00007036699,0.00006290698,0.0002101584,0.00002657373,0.001822678,0.0000623617,0.00007064556,0.00008246356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000467986,"about_ca_system_score_gemma":0.00001272601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007681532,"about_ca_topic_score_gemma":0.00007823059,"domain_scores_codex":[0.9991978,0.00002226717,0.000195603,0.0002441875,0.000170578,0.0001695973],"domain_scores_gemma":[0.9993666,0.00005204194,0.00006013811,0.0003337096,0.0001507138,0.00003684233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009661208,0.0001044586,0.00003988686,0.00003152198,0.00003282928,0.000003328996,0.003374565,0.008355254,0.01454803,0.112754,0.0002739069,0.8603856],"study_design_scores_gemma":[0.000167447,0.0000794243,0.0002211556,0.000004274606,0.000006512433,0.00001315689,0.00006107884,0.9468815,0.01668419,0.03572556,0.00006346938,0.00009220812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01396219,0.000004199568,0.9691697,0.00008149051,0.001126025,0.0004078213,4.664049e-7,0.000301778,0.01494632],"genre_scores_gemma":[0.8750044,7.852307e-7,0.1245722,0.00006013858,0.00007390211,0.00005802569,0.000002410432,0.000009916438,0.0002182242],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9385263,"threshold_uncertainty_score":0.2869483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3042891026","doi":"10.1007/s42452-020-3181-6","title":"Blind copy-move forgery detection using SVD and KS test","year":2020,"lang":"en","type":"article","venue":"SN Applied Sciences","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Test (biology); Artificial intelligence; Pattern recognition (psychology); Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.0411237129089518,"gpt":0.2466280016156089,"spread":0.2055042887066572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003047602,0.0001260269,0.0001338149,0.0001181105,0.0002960649,0.0005167261,0.0004107445,0.00004981787,0.000003257795],"category_scores_gemma":[0.0001613161,0.0001115621,0.00002656468,0.001220347,0.0003625321,0.0007765643,0.0002201205,0.0001071607,0.00003744489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002659994,"about_ca_system_score_gemma":0.00007338468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001063187,"about_ca_topic_score_gemma":0.000009408795,"domain_scores_codex":[0.9985791,0.00001086012,0.0001776103,0.0005392267,0.0004104637,0.0002827029],"domain_scores_gemma":[0.9993913,0.00015995,0.00009754799,0.0001515176,0.00003160266,0.0001680498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000339311,0.00006165252,0.001329358,0.00004412541,0.00001264905,0.00001233922,0.002250087,0.0005474151,0.2414546,0.01685582,0.0001763318,0.7372217],"study_design_scores_gemma":[0.0008754218,0.0006461393,0.001974356,0.00003105142,0.00001696439,0.00008194352,0.000765156,0.5470207,0.4222401,0.02292933,0.00270643,0.0007124261],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7169394,0.00005351334,0.2627778,0.0007651804,0.0008651951,0.0003418248,0.000002458332,0.000323572,0.01793104],"genre_scores_gemma":[0.9865429,0.000002404022,0.01276573,0.0005298221,0.0001335575,0.000008866513,2.539864e-7,0.000005467849,0.0000109827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7365093,"threshold_uncertainty_score":0.4982801,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2611740915","doi":"10.3390/s17051060","title":"A Novel Real-Time Reference Key Frame Scan Matching Method","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Key (lock); Matching (statistics); Computer science; Frame (networking); Key frame; Reference frame; Computer vision; Real-time computing; Artificial intelligence; Mathematics; Computer security; Telecommunications; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02721186157052707,"gpt":0.2918743676559124,"spread":0.2646625060853853,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803513,0.0001573131,0.0001951324,0.00009067333,0.0002878406,0.0007209492,0.001091829,0.00008887256,0.00001134915],"category_scores_gemma":[0.0003393294,0.0001471909,0.00005916536,0.0001033283,0.00008242898,0.000800256,0.0003892947,0.0001858146,0.0005348597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005635645,"about_ca_system_score_gemma":0.00004832127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008777914,"about_ca_topic_score_gemma":0.00005683373,"domain_scores_codex":[0.998634,0.00003996924,0.0001870445,0.0004588168,0.0003483068,0.0003318342],"domain_scores_gemma":[0.998055,0.0001338171,0.0002047006,0.001378289,0.00008215049,0.0001460101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003533002,0.0001361245,0.0001744783,0.00004084645,0.0000780656,0.0001063893,0.004024928,0.000880789,0.185752,0.1180944,0.001053559,0.6896231],"study_design_scores_gemma":[0.003124037,0.0008827165,0.08353052,0.0007200563,0.00007998155,0.001170393,0.0004330266,0.5114317,0.1627141,0.2024721,0.03001704,0.00342433],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5484014,0.000004438542,0.2509481,0.00122555,0.001478206,0.0002161693,0.00001044706,0.000609735,0.1971059],"genre_scores_gemma":[0.5655766,0.000004710032,0.4303823,0.00009769436,0.0001302827,0.000006341314,0.0000016504,0.00002027062,0.003780121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6861988,"threshold_uncertainty_score":0.695213,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111007158","doi":"10.1109/isspit.2010.5711808","title":"Using the local information of image to identify the source camera","year":2010,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Digital camera; Computer science; Computer vision; Noise (video); Identification (biology); Pattern recognition (psychology); Noise reduction; Camera auto-calibration; Image (mathematics); Camera resectioning","retraction":null,"screen_n_in":null,"score":{"opus":0.01113332540427561,"gpt":0.2662059209807495,"spread":0.2550725955764739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002619478,0.00004730858,0.00004404272,0.00004428653,0.00006888824,0.000240965,0.0005113849,0.00002037454,0.000008244916],"category_scores_gemma":[0.0001059249,0.00002428614,0.00002798598,0.0002914948,0.0001044021,0.001260275,0.0002080076,0.0001052514,0.00008977672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009776156,"about_ca_system_score_gemma":0.00002543061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001833959,"about_ca_topic_score_gemma":0.00006375209,"domain_scores_codex":[0.9994493,0.00001310266,0.0001420466,0.00006260206,0.0002319216,0.0001010742],"domain_scores_gemma":[0.9993576,0.00006319665,0.00005853141,0.0003923574,0.00009649439,0.00003184848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004218504,0.00001162211,0.00005479214,0.000006723841,0.000008179033,3.737627e-7,0.004198755,0.0008328756,0.02744686,0.02417988,0.001915006,0.9413407],"study_design_scores_gemma":[0.00027981,0.0001156426,0.006397812,0.00001960039,0.00001256368,0.000123094,0.002157012,0.6536946,0.2818437,0.005472071,0.04961688,0.0002672316],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2040918,5.473507e-7,0.7900103,0.0007917949,0.0006448919,0.0001034385,3.010639e-7,0.00003732529,0.004319604],"genre_scores_gemma":[0.9842011,7.540167e-8,0.01508596,0.0006022858,0.00003404576,0.000003159387,2.149556e-7,0.000002005243,0.00007116685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9410735,"threshold_uncertainty_score":0.2323631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W13310981","doi":"","title":"Detecting differences between photographs and computer generated images","year":2006,"lang":"en","type":"article","venue":"International Conference on Signal Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Gabor filter; Pattern recognition (psychology); Feature extraction; Software; Rendering (computer graphics); Image texture; Feature (linguistics); Image processing; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03106735655855839,"gpt":0.2603812679590382,"spread":0.2293139114004798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001638879,0.0001958539,0.0001729165,0.0002650468,0.0001601094,0.001271799,0.0004960129,0.0000635488,0.00001821564],"category_scores_gemma":[0.00001544327,0.0001749431,0.00004022087,0.000260899,0.0001201781,0.0009300006,0.0001479021,0.0001883584,0.00001402565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003509783,"about_ca_system_score_gemma":0.00005719482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006229724,"about_ca_topic_score_gemma":0.00001002473,"domain_scores_codex":[0.9984701,0.0000382533,0.0002982015,0.0004756128,0.0004842157,0.0002335818],"domain_scores_gemma":[0.9992319,0.00009545398,0.0001824709,0.0001146061,0.000303157,0.00007238813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001379289,0.00004575829,0.009516952,0.00001578865,0.00002648793,0.00002258764,0.000145235,0.00005383014,0.006597907,0.0108474,0.00009310436,0.9726211],"study_design_scores_gemma":[0.0009381996,0.0004716128,0.06483224,0.0004918029,0.00001959723,0.00009749967,0.00009326314,0.7876008,0.07661091,0.06766156,0.0003094822,0.0008730586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3609838,0.00003982462,0.6242327,0.0004005027,0.0003531972,0.0001025156,0.00000661009,0.0002550806,0.01362573],"genre_scores_gemma":[0.9889393,0.000003032722,0.01038981,0.0001071853,0.0004326626,0.00001235592,0.000008649619,0.00001017273,0.00009684229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9717481,"threshold_uncertainty_score":0.999765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376865313","doi":"10.18280/isi.280228","title":"Deep Fake Image Classification Using VGG-19 Model","year":2023,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.05439739302172529,"gpt":0.2700515188614606,"spread":0.2156541258397353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0009120118,0.0003657994,0.0003128453,0.0008039103,0.0005035026,0.001949798,0.0005905766,0.0003155193,0.00002176982],"category_scores_gemma":[0.0009744382,0.0004278091,0.0001596016,0.002564806,0.0004525004,0.02199295,0.0003216683,0.0002858858,0.002630039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194497,"about_ca_system_score_gemma":0.0004446676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001699608,"about_ca_topic_score_gemma":0.00003033959,"domain_scores_codex":[0.9969066,0.0001073068,0.001073757,0.0003548883,0.0007597618,0.0007977118],"domain_scores_gemma":[0.9975488,0.0001225321,0.0006378699,0.0007039881,0.0006995986,0.0002871697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002482166,0.0000403139,0.00007453352,0.0008458285,0.00004873347,0.00001863518,0.02120284,0.1038716,0.0009739596,0.06948858,0.003297999,0.8001121],"study_design_scores_gemma":[0.000335616,0.00007417202,0.001003999,0.000262164,0.00002924781,0.000173479,0.00124828,0.9544986,0.0005911522,0.03633982,0.005029384,0.0004141265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05347194,0.0001795873,0.918133,0.0006078833,0.004684975,0.0005762327,0.00004022647,0.000952511,0.02135368],"genre_scores_gemma":[0.9351497,0.0001353567,0.06169092,0.0004537154,0.0003342051,0.00009932974,0.0002698121,0.00004917783,0.001817768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8816777,"threshold_uncertainty_score":0.9998174,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137826911","doi":"10.1109/icip.2009.5414612","title":"Device temporal forensics: An information theoretic approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Realization (probability); Digital forensics; Network forensics; Computer forensics; Simple (philosophy); Data mining; Markov process; Mathematics; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01049187999559111,"gpt":0.2182427924430177,"spread":0.2077509124474266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001919953,0.000102323,0.00008731783,0.0001095983,0.0000508886,0.0003662869,0.0004173231,0.00004758689,0.000004580524],"category_scores_gemma":[0.00004010823,0.00008287297,0.00003230946,0.0003384126,0.00003757704,0.005441261,0.00004485256,0.00007251057,0.0001486399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002907142,"about_ca_system_score_gemma":0.00003129743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001144931,"about_ca_topic_score_gemma":0.000003395305,"domain_scores_codex":[0.9991683,0.00002088322,0.0001918565,0.0001591051,0.0002667573,0.0001931363],"domain_scores_gemma":[0.9993111,0.00001523804,0.00006278677,0.0004185245,0.00008738263,0.0001048937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003074034,0.00001987766,0.0000200518,0.000003115146,0.00000144949,5.56593e-7,0.0004028253,0.00003703834,0.000008438456,0.4736483,0.0003593713,0.5254959],"study_design_scores_gemma":[0.0007228583,0.0009774439,0.003917031,0.00001430757,0.000008217597,0.0001369373,0.0004296583,0.4873647,0.004532733,0.492342,0.008978736,0.0005753682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01213752,0.000003276919,0.7994689,0.0002713056,0.0002525441,0.0001516825,4.326906e-7,0.0004278864,0.1872864],"genre_scores_gemma":[0.8529043,4.732576e-7,0.1456985,0.001254559,0.00004207935,0.000004756653,0.00001483764,0.000002375719,0.00007814125],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8407668,"threshold_uncertainty_score":0.3944783,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402964300","doi":"10.3390/s24196300","title":"SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Scalability; Data science; Deception; Flexibility (engineering); Analytics; Field (mathematics); Big data; Computer security; Trustworthiness; Benchmark (surveying); Data mining; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.03105017441051629,"gpt":0.2491529323163604,"spread":0.2181027579058441,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002242444,0.0001692499,0.0001456891,0.0001851284,0.00007175725,0.0003947171,0.0005981904,0.00006389301,0.000003224842],"category_scores_gemma":[0.0001100386,0.0001358078,0.00004040304,0.001147173,0.00006637868,0.0009723757,0.000284341,0.0002187043,0.0001565723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007067569,"about_ca_system_score_gemma":0.00006451988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001741357,"about_ca_topic_score_gemma":0.000298662,"domain_scores_codex":[0.9984084,0.00003643545,0.0001869732,0.0006905608,0.0004169249,0.0002606582],"domain_scores_gemma":[0.9984447,0.0001000467,0.00004937252,0.001214796,0.00007450856,0.0001165252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001270247,0.00001569058,0.00001074361,0.00003359423,0.00003798857,0.0001331011,0.0002729509,0.000405055,0.0002389995,0.00132338,0.0002202834,0.9972955],"study_design_scores_gemma":[0.000369833,0.00043993,0.0003848709,0.0002256512,0.00006208065,0.0004589025,0.0001771361,0.8307752,0.01329834,0.003473994,0.1497912,0.0005428348],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4738406,0.0006198324,0.5078253,0.0007276791,0.007806612,0.0004470719,0.00003488433,0.001822169,0.006875857],"genre_scores_gemma":[0.9930423,0.00002690086,0.006342736,0.00006264671,0.0002882238,0.000003697316,0.00001421301,0.00002027931,0.000199017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9967527,"threshold_uncertainty_score":0.5538081,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4382198865","doi":"10.18280/ts.400301","title":"Detecting Deepfakes: A Novel Framework Employing XceptionNet-Based Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02669259494866114,"gpt":0.2407383264036971,"spread":0.2140457314550359,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005155319,0.0002390827,0.0001896872,0.0002748232,0.0003021256,0.0003335303,0.0005266535,0.0001166109,0.00005954716],"category_scores_gemma":[0.0001398941,0.0002478278,0.0001423482,0.00124325,0.00008758402,0.0006516091,0.000143628,0.0003097652,0.0000966361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297634,"about_ca_system_score_gemma":0.00005775603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335486,"about_ca_topic_score_gemma":0.00001457315,"domain_scores_codex":[0.9977235,0.00005450678,0.0004101155,0.0005366055,0.0006563063,0.0006189733],"domain_scores_gemma":[0.9987668,0.0004969608,0.0001498352,0.000305304,0.0001094194,0.0001716346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009003175,0.0002721011,0.004778487,0.00005707957,0.0001013711,0.00006912719,0.0009979268,0.5943463,0.004442675,0.01827246,0.0009793462,0.375593],"study_design_scores_gemma":[0.0006169162,0.0001937809,0.008472702,0.00007009396,0.000009802196,0.0000173504,0.0000569443,0.9878952,0.0004436302,0.001660032,0.0002901738,0.0002733938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.156224,0.00001769391,0.8407336,0.0003839586,0.001289457,0.0002473632,0.000004588145,0.0009824286,0.000116866],"genre_scores_gemma":[0.9765537,7.732833e-7,0.02213546,0.0005920403,0.0005579079,0.00009213777,0.00002250785,0.0000258059,0.00001964451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8203297,"threshold_uncertainty_score":0.9999974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4382345092","doi":"10.1038/s41598-023-37142-9","title":"Deep neural network analysis models for complex random telegraph signals","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Canada First Research Excellence Fund; Industry Canada","keywords":"Computer science; Artificial neural network; Artificial intelligence; Complex network; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.03317681720770999,"gpt":0.2571997950124555,"spread":0.2240229778047456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002424415,0.0001822337,0.0003779657,0.0007562602,0.0004617554,0.001248942,0.0005106067,0.0000573324,0.00001390784],"category_scores_gemma":[0.0001867187,0.0001623657,0.0004679704,0.007970694,0.0001861755,0.001017216,0.0002218009,0.00007284056,0.00003017149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003014376,"about_ca_system_score_gemma":0.00005280566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001148152,"about_ca_topic_score_gemma":0.00006072069,"domain_scores_codex":[0.9967478,0.00005429719,0.000625489,0.001124256,0.0007876692,0.0006605216],"domain_scores_gemma":[0.9976145,0.0002330762,0.0003272394,0.00133019,0.0002969921,0.0001980239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001388703,0.00002818817,0.0003542247,0.00001220234,0.0001818243,0.0001797737,0.0002682085,0.9202656,0.0007553526,0.00136116,0.03815645,0.03842311],"study_design_scores_gemma":[0.0001712292,0.00002583242,0.0003601762,0.000004334868,0.0000639906,0.00003692826,0.00001123705,0.8394898,0.0003270121,0.155478,0.003869568,0.0001619536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03298306,0.00005027874,0.9506979,0.0002059793,0.01337981,0.0006372069,0.000002400265,0.0007328798,0.001310508],"genre_scores_gemma":[0.9825293,0.000001017398,0.01570704,0.00006100436,0.0001702118,0.0001263542,0.0001192857,0.00001525791,0.00127055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9495462,"threshold_uncertainty_score":0.9997879,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4298342869","doi":"10.18280/ria.360407","title":"GAN-Based Encoding Model for Reversible Image Steganography","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Steganography; Encoding (memory); Computer science; Cover (algebra); Image (mathematics); Artificial intelligence; Distortion (music); Pattern recognition (psychology); Metric (unit); Similarity (geometry); Computer vision; Telecommunications; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03984179012861615,"gpt":0.2564952114098063,"spread":0.2166534212811901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000566103,0.0001625679,0.0001761987,0.0003109819,0.0004787143,0.0001806651,0.0009718419,0.00003465982,0.00005351006],"category_scores_gemma":[0.0001240283,0.0001880253,0.0002105771,0.001081472,0.00007453845,0.0005455498,0.0001474162,0.0001892507,0.00006718795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001179206,"about_ca_system_score_gemma":0.000107363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006752532,"about_ca_topic_score_gemma":0.000004879607,"domain_scores_codex":[0.9983095,0.00004232666,0.0003526834,0.0005571722,0.000312648,0.000425694],"domain_scores_gemma":[0.9986899,0.0002325578,0.0001367268,0.0007085314,0.0001234438,0.0001087898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002466703,0.0001736885,0.00002973517,0.00006237966,0.00001170372,0.000009458153,0.001253155,0.9193683,0.009867763,0.01888857,0.002841358,0.04746921],"study_design_scores_gemma":[0.00005442254,0.0001899059,0.000001035748,0.00001526671,0.000005913336,0.00001033534,0.0002885311,0.8817253,0.1022549,0.01057239,0.004682949,0.0001991142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003170865,0.00004749866,0.9875726,0.000717956,0.0009337032,0.0004640306,0.00002033954,0.0002540413,0.006818988],"genre_scores_gemma":[0.9542236,0.0000033287,0.04352507,0.0003428061,0.00004509064,0.0002416707,0.00001000251,0.00002094532,0.00158746],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9510528,"threshold_uncertainty_score":0.7667447,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803263450","doi":"10.1007/s11042-018-5959-8","title":"Video logo removal detection based on sparse representation","year":2018,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Tianjin City; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Logo (programming language); Artificial intelligence; Representation (politics); Popularity; Prior probability; Computer vision; The Internet; Pattern recognition (psychology); Multimedia; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.02858618042671615,"gpt":0.2675505055514261,"spread":0.2389643251247099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001443626,0.0001153967,0.00009885756,0.000118788,0.0001850506,0.000221294,0.0002124314,0.00007133425,0.00001054434],"category_scores_gemma":[0.000166609,0.0001110789,0.00003654406,0.0004675584,0.0001495918,0.0004827919,0.00005944509,0.00009717493,0.0002853001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003584494,"about_ca_system_score_gemma":0.00002342618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001963564,"about_ca_topic_score_gemma":0.00003094072,"domain_scores_codex":[0.9989407,0.0000317335,0.0001835534,0.0004460204,0.000222497,0.0001754844],"domain_scores_gemma":[0.9989055,0.0002368679,0.00008172847,0.0005489352,0.0001115281,0.000115398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001051871,0.0000495313,0.00004949329,0.000003184534,0.000003388299,0.000001343779,0.00008245548,0.00003805588,0.004004069,0.001600719,0.0002352858,0.9939219],"study_design_scores_gemma":[0.0009095657,0.0003825056,0.01256337,0.00002499836,0.00001616248,0.00003989039,0.00004260423,0.7925314,0.1055431,0.006826041,0.08076437,0.0003560161],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01492145,0.000009713093,0.9739227,0.0006380401,0.0003863215,0.0005985636,0.00001130372,0.0003043096,0.009207589],"genre_scores_gemma":[0.9491015,0.000004687091,0.04951931,0.0003663055,0.0004667708,0.0003669199,0.00001913996,0.00001145606,0.0001438802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9935659,"threshold_uncertainty_score":0.4529666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4296700181","doi":"10.1016/j.scijus.2022.09.003","title":"An efficient method to detect series of fraudulent identity documents based on digitised forensic data","year":2022,"lang":"en","type":"article","venue":"Science & Justice","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Internal Security Fund - Police; Israel Science Foundation; Université de Lausanne; European Commission","keywords":"Computer science; False positive paradox; Profiling (computer programming); Forensic science; Information retrieval; Crime scene; Series (stratigraphy); Data mining; Identity (music); Computer security; Artificial intelligence; Criminology; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02542539394928056,"gpt":0.3260618365553703,"spread":0.3006364426060897,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003121339,0.000181873,0.0002115752,0.0005248224,0.0006164453,0.000538778,0.004997359,0.00002116715,0.00002426036],"category_scores_gemma":[0.0008946934,0.0001813282,0.00004381349,0.003398549,0.00030299,0.003500218,0.002353933,0.0001755217,0.00003020718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002673332,"about_ca_system_score_gemma":0.0004351909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009279133,"about_ca_topic_score_gemma":0.00003367887,"domain_scores_codex":[0.9953471,0.0001742811,0.0003464538,0.001146613,0.002451549,0.0005340098],"domain_scores_gemma":[0.996641,0.0001954784,0.0001873283,0.002438761,0.0002134696,0.0003239648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001951254,0.0005580582,0.0001010898,0.00009773835,0.00001053847,0.00006472792,0.001505796,0.4164976,0.02062364,0.0266171,0.0006664959,0.5330622],"study_design_scores_gemma":[0.0005293512,0.002364176,0.00307819,0.00004695314,0.0000519834,0.0000380668,0.0006177201,0.9260166,0.0612888,0.003576449,0.001951104,0.000440632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1035365,0.000007951541,0.8909098,0.00027588,0.002975162,0.0004528151,0.00008537308,0.0001645244,0.001592],"genre_scores_gemma":[0.8167212,3.186363e-7,0.1826064,0.0005514275,0.00003549349,0.00003673814,0.00000809888,0.000009679582,0.00003065291],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7131847,"threshold_uncertainty_score":0.9286419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1906733910","doi":"","title":"Robust detection of copy-move forgery using texture features","year":2011,"lang":"en","type":"article","venue":"Iranian Conference on Electrical Engineering","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Gabor filter; Block (permutation group theory); Pattern recognition (psychology); Feature vector; Computer vision; Feature (linguistics); Lossy compression; Feature extraction; Image (mathematics); Digital image; Matching (statistics); Filter (signal processing); Image processing; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04041664814264905,"gpt":0.2054189295025452,"spread":0.1650022813598962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001096108,0.0002071609,0.0002423769,0.0003170344,0.00004091748,0.00006615563,0.0003933746,0.0001449832,0.000007643894],"category_scores_gemma":[0.0001766139,0.0001993686,0.00008837534,0.0007951966,0.00002752862,0.0004265521,0.00005001216,0.000334739,0.00001093947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009472035,"about_ca_system_score_gemma":0.00006047362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003703539,"about_ca_topic_score_gemma":0.000005439445,"domain_scores_codex":[0.9987069,0.00001857472,0.0002495796,0.0003452678,0.0003038923,0.0003757864],"domain_scores_gemma":[0.9992554,0.00007006014,0.00009698576,0.0003361652,0.0001135389,0.0001279002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001166156,0.0002319493,0.000135554,0.00009298228,0.00008992958,0.00006951602,0.0009545558,0.008051933,0.2241809,0.08141813,0.0000417911,0.6846161],"study_design_scores_gemma":[0.0002726565,0.0004378795,0.003740825,0.00009199155,0.00001282838,0.00007987733,0.00000856872,0.76902,0.2247145,0.001224319,0.00005923843,0.0003373285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05586857,0.00005585252,0.9399624,0.00002296855,0.0006962409,0.00017172,0.000001715341,0.0002989987,0.002921503],"genre_scores_gemma":[0.9896493,0.000005059969,0.01018594,0.0000342014,0.00006371723,0.000006939487,5.522733e-7,0.00001955228,0.00003472823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9337807,"threshold_uncertainty_score":0.8130017,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4233345373","doi":"10.20943/01201706.17","title":"Copy-Move Forgery Detection Based on Enhanced Patch-Match","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Science Issues","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01235251385489955,"gpt":0.2984946380090211,"spread":0.2861421241541215,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001040503,0.0001695743,0.0002227455,0.0006217999,0.0003286822,0.002481658,0.004134764,0.00005065267,0.000007077349],"category_scores_gemma":[0.0004675625,0.0001437633,0.0001289625,0.000222708,0.0003790866,0.004464734,0.0004059303,0.0002234093,0.00005040055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000217614,"about_ca_system_score_gemma":0.000247048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003209599,"about_ca_topic_score_gemma":0.00001023687,"domain_scores_codex":[0.9968423,0.00003403418,0.0004750358,0.0003613177,0.002005947,0.0002813223],"domain_scores_gemma":[0.9967145,0.0001442769,0.0008843936,0.0006028345,0.001475188,0.0001787975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000625316,0.000125613,0.0004386613,0.00000344498,0.00002596075,0.0001004687,0.000323199,0.001685463,0.007134959,0.001331252,0.0002909141,0.9884775],"study_design_scores_gemma":[0.0009173729,0.001067918,0.01562117,0.0003157105,0.000006204214,0.0001906172,0.00001499061,0.3910681,0.5772343,0.009986451,0.003257572,0.0003195725],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1479452,0.00001117431,0.8325371,0.002890113,0.01524343,0.00007072609,0.000001205721,0.00004994997,0.001251123],"genre_scores_gemma":[0.9620829,0.00000889774,0.0363322,0.0005023298,0.001013072,0.000002458106,2.636064e-7,0.000007740065,0.00005014503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.988158,"threshold_uncertainty_score":0.9985539,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}