{"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,"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","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439766,0.0007050339,0.0004320699,0.0006161016,0.000222018,0.000363436,0.0009506589,0.0006152317,0.00131177],"category_scores_gemma":[0.0005612471,0.0002691441,0.000513872,0.0005532612,0.0003219895,0.0007136773,0.0004495929,0.0006466817,0.000576684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00070005,"about_ca_system_score_gemma":0.0007183155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005412411,"about_ca_topic_score_gemma":0.00554652,"domain_scores_codex":[0.9998286,0.00002161035,0.000007884137,0.00004026679,0.00006863972,0.00003300362],"domain_scores_gemma":[0.9998523,0.00002405043,0.00002221519,0.00003273532,0.00005854806,0.00001023871],"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":[0.0003325678,0.0001860729,0.002447909,0.000180881,0.0001484297,0.0003036799,0.00005473544,0.2602854,0.1089922,0.008626915,0.005194554,0.6132466],"study_design_scores_gemma":[0.000005140031,0.00005179843,0.0005829169,0.00000990116,0.0000293666,0.0001188499,0.000006094009,0.973153,0.02355456,0.0006601714,0.001815125,0.00001311782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05272466,0.001125114,0.9395388,0.0002514705,0.0001230551,0.0001027498,0.0001932297,0.001799762,0.00414122],"genre_scores_gemma":[0.6989208,0.001419731,0.2902877,0.0002734149,0.00007277134,0.0001097527,0.0006261779,0.00009835,0.008191298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005412411,"threshold_uncertainty_score":0.0107618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451261910649984,"score_gpt":0.2429311390566931,"score_spread":0.2284185199501932,"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."}}