{"id":"W4405440573","doi":"10.1109/pst62714.2024.10788060","title":"Proactive Audio Authentication Using Speaker Identity Watermarking","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Digital watermarking; Computer science; Authentication (law); Identity (music); Computer security; Speech recognition; Artificial intelligence; Image (mathematics); Acoustics","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":[0.000791078,0.0006318367,0.0005269104,0.0007782624,0.0006020516,0.001205477,0.0008800632,0.001370252,0.002984354],"category_scores_gemma":[0.003911077,0.0002785805,0.000451729,0.000399518,0.0008064228,0.002757751,0.00272412,0.001259442,0.002291807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145243,"about_ca_system_score_gemma":0.000600957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003398604,"about_ca_topic_score_gemma":0.0004426951,"domain_scores_codex":[0.9991321,0.0001549674,0.00004705009,0.0002086317,0.0003369566,0.000120349],"domain_scores_gemma":[0.9984628,0.0003596807,0.0001986002,0.0006834514,0.000220594,0.00007488787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007034325,0.0001710143,0.002126294,0.000129563,0.00006307178,0.0003511557,0.000281583,0.03610316,0.2775312,0.02775317,0.002491422,0.6522949],"study_design_scores_gemma":[0.00004263638,0.0002527415,0.0009750736,0.00003881168,0.0000580003,0.0006256092,0.0001006349,0.6928011,0.2818646,0.01505473,0.008123269,0.00006275281],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05987538,0.0003495419,0.9299573,0.0003406791,0.0001939329,0.00006388302,0.00006165945,0.002448575,0.006709089],"genre_scores_gemma":[0.853722,0.0002716944,0.1386461,0.0001885547,0.0001436242,0.00004836892,0.0001153226,0.00008814815,0.006776199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002984354,"threshold_uncertainty_score":0.009983599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821531990417006,"score_gpt":0.305158063977213,"score_spread":0.2769427440730429,"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."}}