{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009900243,0.0006053056,0.0005404446,0.001066615,0.0007598682,0.001444743,0.001206455,0.001315026,0.007271412],"category_scores_gemma":[0.002946077,0.0003312012,0.0003965122,0.0005837206,0.0007318674,0.002569475,0.001914238,0.0009441364,0.004384443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003326886,"about_ca_system_score_gemma":0.0007686217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003437218,"about_ca_topic_score_gemma":0.0004699572,"domain_scores_codex":[0.9988549,0.0002449521,0.00006690738,0.0001999567,0.0005113677,0.0001219325],"domain_scores_gemma":[0.9989341,0.0002877208,0.000102281,0.0002462244,0.0003561175,0.00007351992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001332992,0.0001412363,0.002330693,0.0005600612,0.00005233147,0.001208279,0.0004958556,0.004499249,0.5632121,0.03885857,0.003219563,0.3840891],"study_design_scores_gemma":[0.0001019649,0.0006855305,0.001392285,0.0001099597,0.0001537903,0.006732528,0.0006156133,0.1329276,0.808903,0.01597613,0.03233027,0.00007133635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03186458,0.0004522676,0.9588246,0.0002050451,0.0002560403,0.0001295447,0.0001095338,0.001596809,0.006561671],"genre_scores_gemma":[0.6911299,0.000758142,0.2984385,0.0002203033,0.0001334394,0.00007971183,0.0002175876,0.0001732883,0.008849069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007271412,"threshold_uncertainty_score":0.02432531,"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."}}