{"id":"W2995555793","doi":"10.1016/j.aci.2019.12.001","title":"Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model","year":2019,"lang":"en","type":"article","venue":"Applied Computing and Informatics","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Inpainting; Discriminative model; Computer science; Artificial intelligence; Generative grammar; Generative model; Probabilistic logic; Pattern recognition (psychology); Mixture model; Bounded function; Support vector machine; Gaussian; Image (mathematics); Statistical model; Machine learning; Computer vision; 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":[0.0007413882,0.0006897624,0.0009939593,0.001015611,0.0002383144,0.0005776148,0.001236831,0.0008787455,0.0005508983],"category_scores_gemma":[0.001033416,0.0004285668,0.001124241,0.0004264761,0.0005625077,0.0009684339,0.0007366075,0.0009390541,0.0003870143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003102113,"about_ca_system_score_gemma":0.0003611351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001275528,"about_ca_topic_score_gemma":0.001429175,"domain_scores_codex":[0.9994593,0.00009884671,0.00002240961,0.0001452297,0.0002012488,0.00007292498],"domain_scores_gemma":[0.9995818,0.0001102766,0.00007320086,0.0001007494,0.0001074616,0.00002653542],"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":[0.000387729,0.0002322277,0.006243522,0.00023972,0.0002397558,0.0004511346,0.0003600332,0.2347327,0.1506937,0.01320441,0.001887528,0.5913276],"study_design_scores_gemma":[0.000005575217,0.00006069374,0.00115163,0.000006539346,0.00002726932,0.0003230941,0.0000197738,0.9750965,0.02073517,0.001906339,0.0006484136,0.0000189092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02136315,0.0001742175,0.9776588,0.00006122986,0.00001720087,0.0000145332,0.00001422015,0.0004176902,0.0002788811],"genre_scores_gemma":[0.6300341,0.0004490478,0.3666192,0.0001548775,0.00005361429,0.00003483363,0.0001308321,0.0001046879,0.002418692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001275528,"threshold_uncertainty_score":0.003920913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396364807436192,"score_gpt":0.2202785375486626,"score_spread":0.2063148894743007,"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."}}