{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005250677,0.0002231048,0.0002681833,0.0002450335,0.0002209324,0.0000815582,0.0006262699,0.0001482034,0.000002079755],"category_scores_gemma":[0.00004139285,0.0001918034,0.0002051296,0.0003655264,0.00003672382,0.000482169,0.00004567191,0.0002436593,0.000001761905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004662242,"about_ca_system_score_gemma":0.00002693561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.680069e-7,"about_ca_topic_score_gemma":2.6676e-7,"domain_scores_codex":[0.9985911,0.000087843,0.000279716,0.0004148584,0.0002757341,0.0003508101],"domain_scores_gemma":[0.9989075,0.0001921919,0.0002010878,0.0005141851,0.0001159301,0.00006907206],"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":[0.001332151,0.001816186,0.003415095,0.001127113,0.0001793772,0.00005652195,0.006252747,0.01761599,0.2784278,0.2000144,0.02965033,0.4601123],"study_design_scores_gemma":[0.001225285,0.001101165,0.002818061,0.0003940477,0.00002773781,0.00000701898,0.0000196065,0.3554501,0.5869371,0.0465418,0.004841786,0.000636309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01762693,0.00004437031,0.9796206,0.0007668698,0.0001258341,0.0004471206,0.000007986916,0.0003849795,0.000975351],"genre_scores_gemma":[0.804768,0.000003542164,0.1946538,0.0003943029,0.00005743058,0.00002814897,0.00001844855,0.00001088828,0.0000653964],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7871411,"threshold_uncertainty_score":0.7821515,"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."}}