{"id":"W2078133453","doi":"10.1016/j.sigpro.2007.07.020","title":"Human visual system based adaptive digital image watermarking","year":2007,"lang":"en","type":"article","venue":"Signal Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Watermark; Digital watermarking; Human visual system model; Computer vision; Artificial intelligence; Computer science; Image quality; Robustness (evolution); Masking (illustration); Digital image; Embedding; Visual masking; Discrete cosine transform; Image (mathematics); Brightness; Mathematics; Image processing; Visual perception","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":[0.0001418292,0.0002372873,0.0001873955,0.0003688381,0.0001370166,0.0003593702,0.0002912182,0.0004844739,0.003927337],"category_scores_gemma":[0.0006110106,0.0000801071,0.0001808804,0.0002254982,0.0002159292,0.0003970377,0.0003644278,0.000248753,0.0008425055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001406332,"about_ca_system_score_gemma":0.0001247261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003893579,"about_ca_topic_score_gemma":0.0005518726,"domain_scores_codex":[0.9998773,0.00002393461,0.000004013585,0.00003277307,0.00004889305,0.00001296711],"domain_scores_gemma":[0.9998313,0.0000631186,0.00001431317,0.00002649463,0.00005629051,0.000008505301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004854941,0.00009657233,0.0008307398,0.0001952801,0.00005799086,0.0001574096,0.0000744942,0.02052262,0.6303927,0.007130662,0.001797898,0.3382582],"study_design_scores_gemma":[0.00005898569,0.0005203265,0.007272894,0.00003944842,0.000114175,0.00140213,0.00004924901,0.5611187,0.4103509,0.004638447,0.01439148,0.00004343209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1816746,0.001692255,0.7930025,0.0002793269,0.0004388219,0.0001246733,0.0001223764,0.001352352,0.02131309],"genre_scores_gemma":[0.8862821,0.0009165205,0.09610523,0.0001308203,0.00007743253,0.0000470314,0.00010916,0.00006794867,0.01626381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003927337,"threshold_uncertainty_score":0.01313829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372581945362532,"score_gpt":0.270652893456114,"score_spread":0.2569270740024887,"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."}}