{"id":"W71685020","doi":"","title":"Audio Zero Watermark Based on Feature of Approximation Signal for Avoiding MP3 Attack","year":2009,"lang":"en","type":"article","venue":"Jisuanji gongcheng","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Alberta Paraplegic Foundation","funders":"","keywords":"Watermark; Computer science; Digital watermarking; Robustness (evolution); Scaling; Algorithm; Code (set theory); Random sequence; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Mathematics; Image (mathematics)","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":[0.0001911504,0.0002949917,0.0003106437,0.000612851,0.0001501472,0.0003383823,0.0003206283,0.000348263,0.0009206811],"category_scores_gemma":[0.000685541,0.0001296648,0.0002699845,0.0004307728,0.0003463826,0.0008708042,0.0002952799,0.0003111176,0.0004247141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001235157,"about_ca_system_score_gemma":0.0002028749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000132965,"about_ca_topic_score_gemma":0.0002228762,"domain_scores_codex":[0.9998418,0.00001749002,0.000009296517,0.00003574642,0.00008112012,0.00001451307],"domain_scores_gemma":[0.9997742,0.00006158013,0.00005280415,0.00003754448,0.00006066151,0.0000132498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003497758,0.00004434518,0.001140343,0.0002703668,0.00003037058,0.0001849558,0.0001046993,0.005895264,0.6363547,0.01190205,0.000558581,0.3431645],"study_design_scores_gemma":[0.0001030831,0.001006201,0.004234632,0.0000481653,0.000140281,0.002307421,0.00008133576,0.2121768,0.7562481,0.005581261,0.01799987,0.00007288707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1455368,0.00147714,0.8490514,0.000173419,0.0001297447,0.00006337994,0.00005299255,0.0006196012,0.002895549],"genre_scores_gemma":[0.7044504,0.001180372,0.2896456,0.00009203672,0.0001103737,0.00005002967,0.0001430903,0.00007907502,0.004249011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009206811,"threshold_uncertainty_score":0.00308001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270559725605753,"score_gpt":0.2785764444530037,"score_spread":0.2558708471969462,"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."}}