{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006175081,0.0004887285,0.0004004666,0.0004037161,0.0003072145,0.0003950504,0.0004026769,0.0007017309,0.0004315602],"category_scores_gemma":[0.002133235,0.000124075,0.0004033662,0.000222942,0.0005103438,0.0005778455,0.0004119519,0.0004326773,0.0001103574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004159212,"about_ca_system_score_gemma":0.0003966752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001385542,"about_ca_topic_score_gemma":0.0008006487,"domain_scores_codex":[0.9991835,0.0002493818,0.00004045475,0.0001349986,0.000298206,0.00009340292],"domain_scores_gemma":[0.9991729,0.0003925148,0.0001249195,0.00013871,0.0001494225,0.00002155865],"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":[0.0006624886,0.0001696694,0.003843265,0.0001203149,0.0001862338,0.0004614682,0.0003027557,0.5604032,0.2266418,0.01208512,0.0006921259,0.1944315],"study_design_scores_gemma":[0.00001214346,0.000136824,0.0006511596,0.000004666173,0.00002180158,0.0001203309,0.00001699918,0.9543341,0.04335707,0.0009831785,0.0003470414,0.00001462826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5218667,0.000290249,0.4744493,0.0002299943,0.00006658836,0.00005327566,0.00002976562,0.0009844642,0.002029724],"genre_scores_gemma":[0.9676404,0.00008133061,0.03156202,0.0000390795,0.000005521222,0.00001892836,0.00001572716,0.00001660228,0.0006204138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001385542,"threshold_uncertainty_score":0.003265738,"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."}}