{"id":"W2057762389","doi":"10.1109/icip.2010.5652589","title":"Watermark survival chance (WSC) concept for improving watermark robustness against JPEG compression","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Watermark; Digital watermarking; Robustness (evolution); Wavelet; JPEG; Discrete wavelet transform; Mathematics; Embedding; Artificial intelligence; Transform coding; Wavelet transform; Computer science; Data compression; Computer vision; Algorithm; Pattern recognition (psychology); Discrete cosine transform; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003409054,0.001157043,0.001100703,0.003120234,0.0006470553,0.001445006,0.001027762,0.001360269,0.001913557],"category_scores_gemma":[0.0139826,0.0003772067,0.0009411424,0.00194182,0.002599813,0.003762958,0.001674855,0.001546776,0.0004372446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026978,"about_ca_system_score_gemma":0.0006274813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004743501,"about_ca_topic_score_gemma":0.0004101526,"domain_scores_codex":[0.9972192,0.0006004312,0.0002550464,0.0004286624,0.001339117,0.0001575491],"domain_scores_gemma":[0.9899459,0.005365808,0.002063983,0.001074656,0.001199472,0.0003501213],"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":[0.0007476403,0.0001302757,0.007105359,0.0007433468,0.0002531461,0.0006484999,0.0003523569,0.2153184,0.1222189,0.2802418,0.0027597,0.3694806],"study_design_scores_gemma":[0.0000675443,0.00122528,0.006253429,0.0001646834,0.0002512521,0.002126343,0.0001214014,0.7927365,0.0830007,0.09635253,0.01735714,0.0003432104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05008559,0.003544934,0.9390458,0.0003984374,0.000272121,0.0001092969,0.000127748,0.0007537334,0.00566239],"genre_scores_gemma":[0.77553,0.00222327,0.218202,0.0002510999,0.0008013136,0.0001994361,0.0002107578,0.000236286,0.002345938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003409054,"threshold_uncertainty_score":0.01802903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304428929799189,"score_gpt":0.2516508207616791,"score_spread":0.2386065314636872,"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."}}