{"id":"W3119660979","doi":"10.18280/ts.370601","title":"Boosting up Source Scanner Identification Using Wavelets and Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Scanner; Computer science; Artificial intelligence; Convolutional neural network; Boosting (machine learning); Pattern recognition (psychology); Block (permutation group theory); Wavelet; Diagonal; Identification (biology); Computer vision; Artificial neural network; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007430373,0.0008899015,0.0006054069,0.0008676738,0.0001833629,0.0005156767,0.0007444368,0.0006899409,0.0009838763],"category_scores_gemma":[0.002004123,0.0003523477,0.0006047889,0.0006349806,0.0003388622,0.00117597,0.0009481704,0.000936071,0.0008625638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003352667,"about_ca_system_score_gemma":0.0003694012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537163,"about_ca_topic_score_gemma":0.001812021,"domain_scores_codex":[0.9997204,0.0000448505,0.00001274637,0.0000627003,0.0001046226,0.00005461864],"domain_scores_gemma":[0.9994254,0.0002245854,0.00007478385,0.0000944358,0.0001525992,0.0000281544],"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":[0.0002975182,0.000172653,0.00460536,0.0001337641,0.0001383125,0.0002527839,0.00008705049,0.2698178,0.05481707,0.006134517,0.002364307,0.6611789],"study_design_scores_gemma":[0.000002985695,0.00004195764,0.0005383212,0.000007651358,0.00001793261,0.00006141426,0.000009739848,0.9887747,0.008417894,0.001428035,0.0006932141,0.000006284203],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0673079,0.0005993296,0.9288714,0.0001562902,0.00007811488,0.00003317221,0.00006178865,0.0009578913,0.001934124],"genre_scores_gemma":[0.7570008,0.000677223,0.2372735,0.000157919,0.00008615392,0.00004504915,0.0003614763,0.0001221825,0.004275684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001537163,"threshold_uncertainty_score":0.003929555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762841135686241,"score_gpt":0.2185169160580843,"score_spread":0.1908885047012219,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}