{"id":"W4403534381","doi":"10.1109/codit62066.2024.10708215","title":"FingFor: a Deep Learning Tool for Biometric Forensics","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; Université Laval","funders":"","keywords":"Biometrics; Computer science; Network forensics; Computer forensics; Deep learning; Artificial intelligence; Digital forensics; Computer security","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":[0.0001510729,0.0001021527,0.00009560444,0.0002387495,0.00007404205,0.0007436842,0.0003326951,0.00003939786,0.000010727],"category_scores_gemma":[0.0000742449,0.00007899192,0.0001175391,0.001386839,0.00002541538,0.0006365085,0.0001707514,0.00007770754,0.0001781875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000240257,"about_ca_system_score_gemma":0.00003297826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003289367,"about_ca_topic_score_gemma":0.000003010939,"domain_scores_codex":[0.9991297,0.000004760137,0.0001415863,0.0002926208,0.0001659705,0.0002653353],"domain_scores_gemma":[0.9995222,0.0001644938,0.00001621806,0.0001899182,0.00005614671,0.00005100972],"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":[6.391873e-7,0.000004724476,0.00001927438,0.00001915185,0.000009920423,0.000004664435,0.00008090137,0.00002632383,0.00001867779,0.3816628,0.002673812,0.6154791],"study_design_scores_gemma":[0.0001330532,0.0001966518,0.00008056051,0.00002380377,0.000008458633,0.00002645871,0.00001862555,0.4482184,0.001223066,0.1231847,0.4266346,0.0002515679],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003313271,0.0003793403,0.97086,0.0004112123,0.0006944196,0.0001176314,0.000002065485,0.000636767,0.0235853],"genre_scores_gemma":[0.8189523,0.00001858822,0.1605673,0.0005034905,0.0001812416,0.00003704394,0.00001299847,0.00002417833,0.01970293],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.815639,"threshold_uncertainty_score":0.7171364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314383854000488,"score_gpt":0.237263902062401,"score_spread":0.2241200635223962,"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."}}