{"id":"W2147237708","doi":"10.1109/tim.2005.855084","title":"An Adaptive Compressed MPEG-2 Video Watermarking Scheme","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Digital watermarking; Watermark; Computer science; Discrete cosine transform; Computer vision; Artificial intelligence; Uncompressed video; Scrambling; Transform coding; Frame (networking); Pixel; Video processing; Image (mathematics); Video tracking; Algorithm; Telecommunications","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":[0.0002711068,0.0001714432,0.0001231042,0.0001969562,0.0003476515,0.0001344601,0.0002435818,0.00005116608,0.00001124514],"category_scores_gemma":[7.019113e-7,0.0001614884,0.00005500158,0.0001663137,0.00004966915,0.001104192,0.000002298507,0.0001476851,0.00000605377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087817,"about_ca_system_score_gemma":0.0000239875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001544734,"about_ca_topic_score_gemma":0.0000303885,"domain_scores_codex":[0.9986596,0.00008634608,0.0002404485,0.0003552455,0.0004441325,0.000214204],"domain_scores_gemma":[0.999392,0.00001263491,0.00007322723,0.0002998644,0.0001039786,0.000118296],"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":[0.0001382938,0.0005216958,0.00005743991,0.00001754567,0.00007241208,0.000003403527,0.001789656,0.002101549,0.08880343,0.002807439,0.00005328745,0.9036338],"study_design_scores_gemma":[0.001600792,0.0005829679,0.0005195487,0.0001069339,0.00002239912,0.00002142791,0.0001565861,0.06888938,0.9231667,0.001027861,0.003497033,0.0004083779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.025872,0.00002417497,0.972525,0.0003773176,0.0002288139,0.00025909,0.000004012471,0.0003535502,0.0003560137],"genre_scores_gemma":[0.8813736,0.00004779538,0.1179864,0.000468881,0.00002534273,0.000073981,0.000001408564,0.000008815025,0.00001369396],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9032255,"threshold_uncertainty_score":0.6585304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755815975122393,"score_gpt":0.2662812150336334,"score_spread":0.2287230552824095,"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."}}