{"id":"W2112938783","doi":"10.1109/tsp.2005.855078","title":"A new framework of LSB steganalysis of digital media","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Steganalysis; Least significant bit; Steganography; Computer science; Artificial intelligence; Cover (algebra); Digital image; Computer vision; Pattern recognition (psychology); Digital media; Image processing; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009879864,0.0001389127,0.0002372974,0.0003315198,0.00009407591,0.00007302206,0.0004801424,0.00008892093,0.00001485358],"category_scores_gemma":[0.000003602568,0.0001254696,0.0001745826,0.0009492947,0.00007128392,0.001073921,0.000002946877,0.0002140494,0.000002092154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001848467,"about_ca_system_score_gemma":0.00008223234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004712431,"about_ca_topic_score_gemma":0.000002905411,"domain_scores_codex":[0.9988618,0.00002021507,0.0003607349,0.0002459187,0.0003353698,0.0001759303],"domain_scores_gemma":[0.9992506,0.0001398798,0.0001839557,0.000266428,0.00007954484,0.00007957577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002744502,0.0001118861,0.00002415078,0.00002631452,0.00003376102,0.000001359036,0.001247144,0.005068953,0.001808966,0.0003444617,0.00000970474,0.9912959],"study_design_scores_gemma":[0.0003499434,0.0002212395,0.00004269154,0.0005806465,0.0000877691,0.00001337847,0.00008400889,0.04008652,0.9232033,0.03456847,0.0003740039,0.0003879734],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00265124,0.0001399047,0.9965307,0.0001409742,0.00003846545,0.00005684334,0.000006521653,0.0001660988,0.0002692959],"genre_scores_gemma":[0.7412226,0.000009058806,0.2586785,0.000026156,0.0000275015,0.000002807735,3.713658e-7,0.000007343423,0.00002566094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9909079,"threshold_uncertainty_score":0.51165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594868181460186,"score_gpt":0.260066189637035,"score_spread":0.2441175078224331,"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."}}