{"id":"W2971967504","doi":"","title":"Application of DenseNet in Camera Model Identification and Post-processing Detection","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; University of Windsor","funders":"","keywords":"Computer science; Identification (biology); Computer vision; Artificial intelligence; Computer graphics (images)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002188498,0.00009901189,0.0001270638,0.0002215752,0.00006058109,0.0001394325,0.00008863619,0.00007216597,0.000001260862],"category_scores_gemma":[0.000004131039,0.00009713102,0.00001882201,0.0001727595,0.00003008901,0.0006706233,0.00006401989,0.00009321706,0.00001086117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001533436,"about_ca_system_score_gemma":0.00002007844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003254737,"about_ca_topic_score_gemma":0.0000151098,"domain_scores_codex":[0.9991057,0.00004582817,0.0002706749,0.0003547421,0.0001134749,0.0001096121],"domain_scores_gemma":[0.9994747,0.00002604931,0.0001754395,0.0001388028,0.0001487711,0.00003625512],"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":[0.000007287252,0.00002022833,0.001747983,0.0001092164,0.000001482345,2.626211e-7,0.0002895161,0.0001350696,0.02617496,0.000005667578,0.000001807183,0.9715065],"study_design_scores_gemma":[0.0003422358,0.00007057715,0.0331769,0.0001218412,0.000004097944,0.00005395311,0.00002254854,0.9566916,0.007977056,0.00141958,0.000005429361,0.000114174],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.464267,0.00005024435,0.5353989,0.0000682414,0.00008380691,0.00009154618,0.000001303766,0.00002621602,0.00001269687],"genre_scores_gemma":[0.9855259,0.00003577209,0.01422444,0.0001449212,0.00002685193,0.000008324439,0.00001788907,0.000006307102,0.000009576282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9713923,"threshold_uncertainty_score":0.3960888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042703376764862,"score_gpt":0.2380472585473151,"score_spread":0.2276202247796665,"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."}}