{"id":"W2002732647","doi":"10.1109/mmsp.2005.248660","title":"Steganalysis of Degraded Document Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Steganalysis; Steganography; Computer science; Artificial intelligence; Embedding; Pixel; Distortion (music); Noise (video); Pattern recognition (psychology); Statistic; Computer vision; Process (computing); Binary number; Image (mathematics); Mathematics; Bandwidth (computing); Statistics; Telecommunications; Arithmetic","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":[0.0002949726,0.0003188845,0.0006192399,0.0004253324,0.0001164166,0.0003433362,0.0002244846,0.0003851865,0.0003636502],"category_scores_gemma":[0.001870491,0.0001141272,0.0002657505,0.0002411291,0.0004442825,0.0004901448,0.0002211761,0.0004137774,0.0001703345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001906642,"about_ca_system_score_gemma":0.0001361459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002661374,"about_ca_topic_score_gemma":0.0002863505,"domain_scores_codex":[0.9996982,0.00004982173,0.00001548675,0.00005286156,0.0001576867,0.00002607249],"domain_scores_gemma":[0.9990275,0.0005306677,0.000154795,0.0001192952,0.0001441877,0.00002349618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000626423,0.00006143757,0.004227128,0.0002904125,0.00009434764,0.0006948796,0.0001462993,0.04571943,0.7934315,0.003674041,0.0003095428,0.1507247],"study_design_scores_gemma":[0.00002428932,0.0004320552,0.008956281,0.00003753789,0.00007583194,0.002524346,0.00007314454,0.4061577,0.5771358,0.002226206,0.002316722,0.00004009416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.534971,0.001480424,0.4606147,0.0001584039,0.00006176327,0.00004133075,0.00006913761,0.0005705279,0.00203272],"genre_scores_gemma":[0.9385098,0.0006301009,0.05948506,0.00006038337,0.00003879265,0.0000164732,0.00008571443,0.0000322154,0.001141347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006192399,"threshold_uncertainty_score":0.001559973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009348192072816432,"score_gpt":0.2519655190449504,"score_spread":0.242617326972134,"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."}}