{"id":"W2532797029","doi":"10.1109/itw2.2006.323808","title":"On Joint Compression and Information Embedding When Watermarks and Covertexts Are Correlated","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Digital watermarking; Embedding; Watermark; Distortion (music); Combinatorics; Source model; Algorithm; Computer science; Discrete mathematics; Mathematics; Artificial intelligence; Theoretical computer science; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002471566,0.001604165,0.001641308,0.000833118,0.000691167,0.001506701,0.0008878075,0.001338176,0.001347433],"category_scores_gemma":[0.01199572,0.0008635017,0.0008270118,0.001459182,0.002679269,0.004282196,0.0021831,0.001052879,0.0004836847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050641,"about_ca_system_score_gemma":0.001252516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001492471,"about_ca_topic_score_gemma":0.001294444,"domain_scores_codex":[0.9965479,0.001197897,0.0001635366,0.0005743958,0.00103982,0.0004764494],"domain_scores_gemma":[0.9907073,0.006149602,0.001359682,0.0008550474,0.0007516867,0.0001765904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001623352,0.000223553,0.002923939,0.0009251373,0.0003554046,0.002288553,0.0007649882,0.618643,0.04611181,0.1810537,0.001712992,0.1433734],"study_design_scores_gemma":[0.00004919503,0.0004480649,0.0009092519,0.00003978817,0.0001536503,0.0008110019,0.00009305195,0.9546453,0.0176027,0.02396777,0.001218895,0.00006129262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07667825,0.001563526,0.9163269,0.000313138,0.00006178985,0.0001107585,0.00004817727,0.0001920294,0.004705434],"genre_scores_gemma":[0.931607,0.002607214,0.06048514,0.0001002858,0.0002621899,0.0001217347,0.0001331582,0.00006704176,0.004616261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002471566,"threshold_uncertainty_score":0.01307106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006012628981859774,"score_gpt":0.2073610667826583,"score_spread":0.2013484378007986,"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."}}