{"id":"W1512831777","doi":"10.1109/icip.2001.959171","title":"A content dependent spatially localized video watermark for resistance to collusion and interpolation attacks","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Digital watermarking; Watermark; Computer science; Bilinear interpolation; Computer vision; Subframe; Artificial intelligence; Collusion; Redundancy (engineering); Invisibility; Algorithm; Embedding; Theoretical computer science; Computer security; Image (mathematics); 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":[0.0002095117,0.0001406492,0.0001643083,0.0001295861,0.0001707432,0.0001391161,0.0003520765,0.00005618846,0.00001044677],"category_scores_gemma":[0.00003271492,0.0001134878,0.00005393758,0.0001570527,0.00002857847,0.0003778657,0.0002276691,0.00005299077,0.000003754806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003121404,"about_ca_system_score_gemma":0.000004653189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001523286,"about_ca_topic_score_gemma":0.0001305949,"domain_scores_codex":[0.9989068,0.00004175736,0.0002582578,0.0003884108,0.000166478,0.0002383157],"domain_scores_gemma":[0.9993136,0.00007884849,0.00006723984,0.00033239,0.0001046341,0.0001032941],"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":[0.002436933,0.0008102039,0.01482914,0.0005882919,0.0002214944,0.0001147945,0.01428847,0.0002103847,0.1885038,0.2307137,0.0501546,0.4971281],"study_design_scores_gemma":[0.006263819,0.001979955,0.005138984,0.0009126632,0.00005183448,0.00005911956,0.0001062533,0.3729819,0.2891166,0.06405314,0.2570764,0.002259382],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007674221,0.00008880164,0.9883642,0.001500305,0.0001297179,0.0006831279,0.000004428969,0.0002970124,0.001258224],"genre_scores_gemma":[0.6687922,0.00002980265,0.3287553,0.0007631847,0.00001863156,0.00009239501,0.000001696961,0.000007256555,0.001539522],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.661118,"threshold_uncertainty_score":0.4627898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03314917731955945,"score_gpt":0.2576426185323381,"score_spread":0.2244934412127786,"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."}}