{"id":"W2941649104","doi":"10.1109/tifs.2019.2956591","title":"A Robust Approach for Securing Audio Classification Against Adversarial Attacks","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Information Forensics and Security","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spectrogram; Artificial intelligence; Codebook; Audio signal; Speech recognition; Deep learning; Smoothing; Pattern recognition (psychology); Preprocessor; Support vector machine; Feature learning; Machine learning; Speech coding; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004256102,0.0004314864,0.0004274638,0.0004588811,0.0003027965,0.0008171882,0.0004990955,0.0005410174,0.000001884498],"category_scores_gemma":[0.00002586642,0.0004524355,0.0002819881,0.0002740671,0.0001094549,0.00243741,0.00003455443,0.0008119927,0.00002500363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000247637,"about_ca_system_score_gemma":0.0002105132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001915515,"about_ca_topic_score_gemma":0.00001081649,"domain_scores_codex":[0.9977384,0.00004249957,0.00073468,0.0005745442,0.0005317982,0.0003780867],"domain_scores_gemma":[0.9979427,0.0001240996,0.0005099369,0.0008331581,0.0004193938,0.0001707433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002816894,0.0002443903,0.000008692336,0.00170037,0.0003030309,8.95977e-7,0.009936152,0.531021,0.0000240839,0.02755037,0.002440365,0.426489],"study_design_scores_gemma":[0.001064326,0.0001602054,0.00003359791,0.00009767264,0.00004975859,0.000009404558,0.0002421983,0.9872352,0.0009073757,0.006280759,0.003399036,0.000520469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005342526,0.00001393087,0.982161,0.0001805639,0.005221122,0.001701413,0.000340624,0.0003350134,0.004703827],"genre_scores_gemma":[0.9600915,0.00008822195,0.03859533,0.0002905846,0.0001498752,0.0003520038,0.0003621924,0.00002480884,0.00004545167],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.954749,"threshold_uncertainty_score":0.9997928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03006476488899338,"score_gpt":0.2290495888172815,"score_spread":0.1989848239282881,"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."}}