{"id":"W3094464978","doi":"10.1109/icassp39728.2021.9413626","title":"Class-Conditional Defense GAN Against End-To-End Speech Attacks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Sentence; SIGNAL (programming language); Generative adversarial network; Speech recognition; Spectrogram; Discriminator; Adversarial system; Generative grammar; Algorithm; Artificial intelligence; Deep learning; 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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003237388,0.0004573204,0.0004523043,0.0003926274,0.00009358584,0.001361312,0.001361102,0.0003603912,0.0002952593],"category_scores_gemma":[0.0002619572,0.0004720498,0.0003143543,0.0005253047,0.0001078135,0.0006496867,0.002414,0.0007840359,0.0009549285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003019164,"about_ca_system_score_gemma":0.0005644887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005288262,"about_ca_topic_score_gemma":0.000231459,"domain_scores_codex":[0.9963333,0.00007727611,0.0005212944,0.001427947,0.001094172,0.0005460345],"domain_scores_gemma":[0.9972351,0.000179431,0.0001834697,0.001605059,0.0003846925,0.0004122359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002292175,0.0004370612,0.0002419455,0.0002052189,0.0003588613,0.001753518,0.001141147,0.006520975,0.002005801,0.06720287,0.05242134,0.8676884],"study_design_scores_gemma":[0.003095238,0.001007937,0.01574007,0.001888969,0.0002051006,0.002214054,0.0009029884,0.125274,0.2831087,0.1968826,0.3601303,0.009550062],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1292047,0.00005398633,0.4987233,0.002890796,0.01004392,0.0007054175,0.00009355642,0.00104234,0.357242],"genre_scores_gemma":[0.9236841,0.00001104101,0.06873886,0.003133683,0.0005667294,0.00009098579,0.0004200066,0.0000411024,0.003313513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8581383,"threshold_uncertainty_score":0.9998229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01913107164434199,"score_gpt":0.2530109474255905,"score_spread":0.2338798757812485,"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."}}