{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000120342,0.0003839489,0.0003569926,0.0007533533,0.0004779267,0.0003375552,0.0007626295,0.0001735861,0.000002070719],"category_scores_gemma":[0.00001373435,0.0003832694,0.0001134468,0.0005926805,0.00008276862,0.0005462242,0.00009330106,0.0003719853,0.000001501149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007056863,"about_ca_system_score_gemma":0.0002475624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002111811,"about_ca_topic_score_gemma":0.0001041849,"domain_scores_codex":[0.9978991,0.000006280698,0.00029656,0.0007204777,0.0002202805,0.0008573139],"domain_scores_gemma":[0.9987963,0.00003225863,0.00007602983,0.0001768464,0.0003142974,0.0006042568],"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":[0.00001333383,0.00006509163,0.001187784,0.00006863782,0.00004583332,0.00002310745,0.001122899,0.0003606393,0.00149499,0.9696634,0.0007709879,0.02518326],"study_design_scores_gemma":[0.0002887646,0.000477133,0.001153222,0.00007593745,0.000006627358,0.00005359819,0.000003510233,0.9851556,0.0006332538,0.01113023,0.0005026833,0.000519422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0459618,0.00004141751,0.9520705,0.0003561147,0.00007659969,0.000387265,0.000005876023,0.0005294285,0.0005709555],"genre_scores_gemma":[0.9474487,0.00004638686,0.05179691,0.0003937826,0.00008785086,0.0001298633,0.000004228018,0.00002306755,0.00006918778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.984795,"threshold_uncertainty_score":0.9998619,"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."}}