{"id":"W2359252321","doi":"","title":"New digital watermarking algorithm for audio authentication","year":2012,"lang":"en","type":"article","venue":"Jisuanji yingyong yanjiu","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":"Digital watermarking; Computer science; Watermark; Discrete cosine transform; Frame (networking); Authentication (law); Code (set theory); Digital audio; Set (abstract data type); Algorithm; Computer vision; Artificial intelligence; Audio signal; Speech recognition; Computer security; Image (mathematics); Speech coding; Computer network","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":[0.0003360588,0.0002366362,0.0002105115,0.0002020891,0.0002761309,0.000392389,0.0007849481,0.0001118695,0.000007048031],"category_scores_gemma":[0.0000297898,0.0002136207,0.0001897198,0.0003290293,0.00003779203,0.002440011,0.0002520726,0.0001352702,0.00003982894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000583408,"about_ca_system_score_gemma":0.00003422512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006401468,"about_ca_topic_score_gemma":2.829167e-7,"domain_scores_codex":[0.9983181,0.00002579194,0.0003170976,0.0003836706,0.0002574013,0.0006978955],"domain_scores_gemma":[0.9988279,0.000111497,0.0001528202,0.0005939495,0.00007792901,0.0002359193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006831821,0.00008128805,0.004491575,0.00002465734,0.00003733546,0.00000212049,0.001418683,0.000002185511,0.001223372,0.01623632,0.002422541,0.9740531],"study_design_scores_gemma":[0.002565578,0.0004986286,0.01786547,0.0004175874,0.00013291,0.000228407,0.0001675078,0.08484245,0.1603941,0.1634952,0.5662329,0.003159305],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003492855,0.000187083,0.9931269,0.0002475783,0.0007391935,0.0003572893,0.00001065377,0.0008930829,0.0009454195],"genre_scores_gemma":[0.546922,0.000008541134,0.4517862,0.0001176084,0.0004138004,0.00004211545,0.00002438963,0.00002367123,0.0006616221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9708938,"threshold_uncertainty_score":0.8711202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848819721065055,"score_gpt":0.2708500837769501,"score_spread":0.2523618865662995,"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."}}