{"id":"W1968925786","doi":"10.4018/ijdcf.2013100104","title":"Audio Watermarking Scheme Using IMFs and HHT for Forensic Applications","year":2013,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Digital watermarking; Computer science; Hilbert–Huang transform; Watermark; Audio signal; Authentication (law); Speech recognition; SIGNAL (programming language); Fidelity; Frame (networking); Transformation (genetics); Computer security; Artificial intelligence; Computer vision; Image (mathematics); Speech coding; Telecommunications; Filter (signal processing)","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":[0.00008739718,0.0001057406,0.0001366036,0.0001557845,0.00007816481,0.000513502,0.0003337478,0.00004003627,6.566253e-7],"category_scores_gemma":[0.00002033505,0.00008594916,0.00008267721,0.00006235251,0.00009713204,0.001848262,0.0001627585,0.00008592902,4.891341e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002023532,"about_ca_system_score_gemma":0.00002104324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002215109,"about_ca_topic_score_gemma":2.101097e-7,"domain_scores_codex":[0.9992429,0.000005304103,0.0002906591,0.0001362504,0.0001865752,0.0001382989],"domain_scores_gemma":[0.9990393,0.00006915434,0.0002092982,0.0001007861,0.0005059104,0.00007555098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007065576,0.00009597025,0.01120961,0.00005722103,0.0003774184,0.00003460452,0.0006542706,0.0000179007,0.009026243,0.1754729,0.001121698,0.8018615],"study_design_scores_gemma":[0.001002859,0.000289693,0.002319315,0.0001797243,0.00003411395,0.001565237,0.00009191194,0.01681496,0.01575908,0.9461328,0.01540493,0.0004054159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05931823,0.0002397456,0.9392772,0.000355306,0.000191614,0.0001646029,0.00001045081,0.00003040626,0.0004124399],"genre_scores_gemma":[0.7726426,0.00003355319,0.227028,0.0001245777,0.0001345881,0.000007660163,0.00000432097,0.000007084023,0.00001757022],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8014561,"threshold_uncertainty_score":0.4951712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02011521386352126,"score_gpt":0.2751941016228266,"score_spread":0.2550788877593053,"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."}}