{"id":"W4360605113","doi":"10.1109/icnc57223.2023.10074246","title":"Shallow- and Deep- fake Image Manipulation Localization Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Artificial intelligence; Deep learning; Computer science; Task (project management); Code (set theory); Inference; Image (mathematics); Computer vision; Image manipulation; Image editing; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000754744,0.002137358,0.001303885,0.001515014,0.0006015022,0.001505103,0.002279584,0.00199949,0.00354415],"category_scores_gemma":[0.003520773,0.0005750731,0.001150707,0.0009220313,0.0008051129,0.00327777,0.002130103,0.002640459,0.002583008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012008,"about_ca_system_score_gemma":0.001089307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005417444,"about_ca_topic_score_gemma":0.008425254,"domain_scores_codex":[0.9992969,0.0000970558,0.00003727901,0.0002162468,0.0001850269,0.0001675092],"domain_scores_gemma":[0.9986994,0.0003188957,0.0001993399,0.0005059398,0.0001946725,0.00008171958],"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":[0.000834337,0.0006108577,0.0117132,0.0006735135,0.0003365241,0.0007225676,0.0002128733,0.1037797,0.0188827,0.007950677,0.05235291,0.8019301],"study_design_scores_gemma":[0.00002801216,0.0001194219,0.001973791,0.00009029786,0.00006067112,0.0003582444,0.0001045487,0.9597118,0.01817954,0.01279669,0.006542144,0.00003476926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2196337,0.005097475,0.726429,0.002387417,0.0005892258,0.000398889,0.006716441,0.02623791,0.01250992],"genre_scores_gemma":[0.7254283,0.001554274,0.2416149,0.0007881823,0.0001826476,0.0001974328,0.01485515,0.0004948272,0.01488437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005417444,"threshold_uncertainty_score":0.01185638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041804617560285,"score_gpt":0.2444755988105637,"score_spread":0.2240575526349609,"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."}}