{"id":"W4413211245","doi":"10.1007/978-3-031-94962-3_2","title":"Leveraging Bayer Pattern Analysis for Authenticity Detection of Real and Fake Images","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Information retrieval; Artificial intelligence","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":[0.0005752071,0.000697131,0.0006313364,0.002109875,0.0003472074,0.001284008,0.0007028081,0.0009304247,0.002971572],"category_scores_gemma":[0.002195845,0.0003278952,0.0004291772,0.001173955,0.000515457,0.001716983,0.001312795,0.0009155649,0.00245424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002273882,"about_ca_system_score_gemma":0.0002608603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004772277,"about_ca_topic_score_gemma":0.0008540184,"domain_scores_codex":[0.9993468,0.00006928037,0.00002694146,0.0001067571,0.0003805972,0.00006960308],"domain_scores_gemma":[0.9989329,0.0003168978,0.0001560807,0.0002773784,0.00027616,0.00004055294],"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":[0.0003639349,0.0001666839,0.003582602,0.0001549143,0.00005389019,0.0004339068,0.0001779068,0.008035331,0.2115641,0.007912614,0.00413279,0.7634212],"study_design_scores_gemma":[0.00002571039,0.0003195528,0.007166758,0.00004593052,0.00008192586,0.00292688,0.0001692038,0.7551786,0.2026908,0.01926678,0.01205814,0.00006974847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09890573,0.0009679123,0.8883153,0.000324006,0.0001684408,0.0001083692,0.0002353943,0.002458879,0.008516016],"genre_scores_gemma":[0.5603136,0.001126858,0.4256952,0.0002097084,0.0001470317,0.00005825139,0.0005655804,0.0002870501,0.01159677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002971572,"threshold_uncertainty_score":0.009940863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02760109058008395,"score_gpt":0.2741720442620276,"score_spread":0.2465709536819437,"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."}}