{"id":"W1976648206","doi":"10.1109/ccece.2008.4564703","title":"A human visual model for steganography","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Invisibility; Steganography; Cover (algebra); Human visual system model; Computer science; Robustness (evolution); Information hiding; Computer vision; Image (mathematics); Artificial intelligence; Image quality; Steganography tools; Pixel; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004742095,0.0006827358,0.0003693633,0.0006213765,0.0003171427,0.001100801,0.001151147,0.001535898,0.00740958],"category_scores_gemma":[0.001159633,0.000159412,0.0005501589,0.0004768774,0.001189035,0.001534764,0.0006280668,0.001248101,0.002332497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006462033,"about_ca_system_score_gemma":0.0004707732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428567,"about_ca_topic_score_gemma":0.001579469,"domain_scores_codex":[0.999604,0.0001364172,0.0000127776,0.00007811487,0.0001331483,0.00003558342],"domain_scores_gemma":[0.9996475,0.0001511694,0.00002870427,0.00005498897,0.00009456297,0.0000229597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002942267,0.0001510414,0.0009351828,0.0005958585,0.00008618073,0.0006978303,0.0006394086,0.07282017,0.03325003,0.7230743,0.01781109,0.1496447],"study_design_scores_gemma":[0.00009795362,0.0006563629,0.001765671,0.0002265651,0.00006623848,0.00206644,0.0002291127,0.5548322,0.007753227,0.3262778,0.1059225,0.000106079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01367227,0.004485393,0.8941005,0.003124858,0.0008486374,0.000254861,0.0004071867,0.001234709,0.0818716],"genre_scores_gemma":[0.7251605,0.00585441,0.1993546,0.001634136,0.0005709793,0.000569408,0.0003967065,0.0001883418,0.06627098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00740958,"threshold_uncertainty_score":0.02478749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251857651199914,"score_gpt":0.2325213715287809,"score_spread":0.2073356064087895,"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."}}