{"id":"W2956804760","doi":"10.1109/icc.2019.8761244","title":"Spoofing Attacks on Speaker Verification Systems Based Generated Voice using Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Spoofing attack; Speaker verification; Computer science; Genetic algorithm; Authentication (law); Speaker recognition; Speech recognition; Population; Algorithm; Pattern recognition (psychology); Artificial intelligence; Machine learning; Computer network; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002123793,0.0001338797,0.0001442084,0.0001461926,0.00007938086,0.000254328,0.0003175253,0.00007110664,0.000165053],"category_scores_gemma":[0.00001636245,0.0001199451,0.00005120053,0.0003504585,0.00001056028,0.0001834016,0.00002852862,0.00007141344,0.001804227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007886018,"about_ca_system_score_gemma":0.00006280969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001051065,"about_ca_topic_score_gemma":0.000001314136,"domain_scores_codex":[0.9987419,0.0001115796,0.0002266376,0.0004110508,0.0002938629,0.0002149497],"domain_scores_gemma":[0.9991051,0.00008633977,0.00008581347,0.0005222713,0.0001223057,0.00007819898],"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":[0.00002766475,0.0004455153,0.003480074,0.0001165833,0.0001079639,0.00007026157,0.0002407427,0.05491598,0.09841798,0.003586026,0.002332886,0.8362583],"study_design_scores_gemma":[0.0002311055,0.00003041363,0.002042325,0.00003658234,0.000005309841,0.00001545718,0.00001857915,0.9730899,0.02216844,0.000009404011,0.002173882,0.0001785865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1440873,0.00002873862,0.8490131,0.0000946073,0.0007927002,0.0002476157,0.000002473759,0.0001908356,0.005542621],"genre_scores_gemma":[0.5082011,0.000004029691,0.4896621,0.0008438576,0.0001150497,0.00001096476,0.000008027039,0.00002048016,0.001134398],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9181739,"threshold_uncertainty_score":0.998973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678619749085209,"score_gpt":0.2540703735878523,"score_spread":0.2172841760970002,"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."}}