{"id":"W4324353508","doi":"10.1016/j.fsidi.2023.301539","title":"Transformer for authenticating the source microphone in digital audio forensics","year":2023,"lang":"en","type":"article","venue":"Forensic Science International Digital Investigation","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"King Saud University","keywords":"Computer science; Microphone; Digital audio; Digital forensics; Transformer; Sound recording and reproduction; Speech recognition; Network forensics; Architecture; Artificial intelligence; Audio signal; Speech coding; Computer security; Acoustics; Engineering; Telecommunications","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007096828,0.0002156922,0.000157971,0.0006224622,0.0002695009,0.00243022,0.001592946,0.00006141759,0.000002528455],"category_scores_gemma":[0.001848796,0.0001734771,0.0001229022,0.002849934,0.001242818,0.006580649,0.0002342396,0.0001577691,0.0002541541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002492807,"about_ca_system_score_gemma":0.000233611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001640848,"about_ca_topic_score_gemma":0.00001737123,"domain_scores_codex":[0.997185,0.00000755689,0.0005189815,0.000661528,0.001050707,0.0005762366],"domain_scores_gemma":[0.9984339,0.0003342514,0.0001894057,0.0004361865,0.0004507127,0.0001554804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003268545,0.00005543191,0.004964528,0.00002598996,0.0000360075,0.000009179651,0.00527558,0.002053837,0.01285985,0.0920209,0.003320141,0.8793459],"study_design_scores_gemma":[0.0009741095,0.0002092183,0.01505484,0.0001684932,0.00000793798,0.00009106488,0.0007536397,0.4050843,0.06294592,0.5071841,0.006908692,0.0006176623],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7821332,0.000007955014,0.2020843,0.0050001,0.003549695,0.0007205347,0.00006158838,0.0004275008,0.006015213],"genre_scores_gemma":[0.9942988,0.000001687853,0.003767964,0.0003107516,0.000166743,0.0001130668,0.0001011664,0.00002069146,0.001219166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8787282,"threshold_uncertainty_score":0.9986054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045034900493485,"score_gpt":0.252061104442254,"score_spread":0.2316107554373192,"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."}}