{"id":"W2157897932","doi":"10.1109/tifs.2010.2051255","title":"A Wavelet-PCA-Based Fingerprinting Scheme for Peer-to-Peer Video File Sharing","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Robustness (evolution); Fingerprint (computing); Wavelet; Principal component analysis; Data mining; Artificial intelligence; Pattern recognition (psychology); Embedding; Fingerprint recognition; Representation (politics)","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":[0.000397615,0.0001593751,0.0001390047,0.00026648,0.0004585088,0.000312791,0.0003067392,0.0001150634,0.00001858816],"category_scores_gemma":[0.00004289312,0.0001567755,0.0001079803,0.0002810478,0.00004482296,0.001309151,0.000008925266,0.0003426879,0.000006963693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001664986,"about_ca_system_score_gemma":0.00002944335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001657139,"about_ca_topic_score_gemma":0.00002553001,"domain_scores_codex":[0.9989291,0.000007232286,0.0003046887,0.0002113313,0.0002920633,0.0002555208],"domain_scores_gemma":[0.9988826,0.0001172203,0.0001027215,0.0003652259,0.0004172089,0.0001150034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002440712,0.0002423459,0.0001126475,0.0004540658,0.0001006669,0.00000311048,0.01578457,0.003837141,0.00280456,0.1900746,0.01466161,0.7716806],"study_design_scores_gemma":[0.0008140178,0.0002391684,0.0001456216,0.00008023148,0.00001316084,0.00001255149,0.00007082328,0.7133312,0.1048197,0.04430917,0.1355875,0.0005768962],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01544409,7.597459e-7,0.9818631,0.0009692642,0.00043534,0.0003494629,0.0001896074,0.0003214171,0.0004269641],"genre_scores_gemma":[0.7062694,0.000001054662,0.2928797,0.0006423767,0.00002191278,0.000117213,0.00003083537,0.000006469312,0.00003097669],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7711037,"threshold_uncertainty_score":0.6393118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089304094986136,"score_gpt":0.2436871102690021,"score_spread":0.2327940693191408,"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."}}