{"id":"W2526347824","doi":"","title":"Detection of LSB steganography via sample pair analysis","year":2003,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Least significant bit; Computer science; Steganography; Steganalysis; Robustness (evolution); Embedding; Artificial intelligence; Sample (material); Algorithm; Pattern recognition (psychology)","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":[0.0003293548,0.0006173998,0.0007460098,0.001250592,0.0002424595,0.0005075822,0.000347762,0.0005935987,0.001764889],"category_scores_gemma":[0.001499058,0.000312401,0.0002903352,0.0006132945,0.0003248744,0.000738431,0.000602002,0.000520004,0.0009203189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967548,"about_ca_system_score_gemma":0.0002212223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001449852,"about_ca_topic_score_gemma":0.0002475083,"domain_scores_codex":[0.9995197,0.0001188184,0.0000132601,0.00006854552,0.0002375216,0.0000422254],"domain_scores_gemma":[0.9991436,0.0004413963,0.0001330215,0.00008208985,0.0001602995,0.00003957079],"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":[0.001441316,0.0001304182,0.006109191,0.0001474709,0.00007793985,0.000341078,0.0001313521,0.004847003,0.7529032,0.002278128,0.000787279,0.2308057],"study_design_scores_gemma":[0.00007267724,0.0009755936,0.0169014,0.00002085891,0.00009669634,0.002461813,0.00009866013,0.384909,0.5893087,0.002344698,0.002757269,0.000052536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5026098,0.0003930721,0.4920505,0.0001991559,0.00009588592,0.00008830771,0.0001520553,0.001485135,0.002926165],"genre_scores_gemma":[0.8788196,0.0001959501,0.1185086,0.00005862899,0.00005430228,0.00005868465,0.0001314171,0.00009084836,0.002082054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001764889,"threshold_uncertainty_score":0.005904138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009573288283020683,"score_gpt":0.2396500249214193,"score_spread":0.2300767366383986,"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."}}