{"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,"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)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"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"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03106735655855839,"score_gpt":0.2603812679590382,"score_spread":0.2293139114004798,"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."}}