{"id":"W2102511552","doi":"10.1109/tsp.2003.812753","title":"Detection of LSB steganography via sample pair analysis","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":522,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Least significant bit; Steganalysis; Steganography; Robustness (evolution); Computer science; Embedding; Digital watermarking; Artificial intelligence; Sample (material); Pattern recognition (psychology); Mathematics; Algorithm; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006513043,0.000603326,0.0007830149,0.001138022,0.0002135866,0.0005390612,0.0005719821,0.0006880229,0.0007254318],"category_scores_gemma":[0.004490405,0.0004196987,0.0003257037,0.0004880234,0.0008879966,0.001340904,0.001032587,0.0008196345,0.0004562728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213398,"about_ca_system_score_gemma":0.0002040922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009945746,"about_ca_topic_score_gemma":0.0001318262,"domain_scores_codex":[0.9990671,0.0002225739,0.00002643023,0.0001200803,0.000528584,0.00003529434],"domain_scores_gemma":[0.9977803,0.001357533,0.0003200008,0.0002390997,0.000251252,0.00005191889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001072384,0.0001154169,0.006040957,0.0004163768,0.0001458912,0.0004512107,0.0002910763,0.04608296,0.5685583,0.02090738,0.0008494529,0.3550685],"study_design_scores_gemma":[0.00004780311,0.0005854433,0.004766638,0.00002910442,0.00005945841,0.002076589,0.00006593973,0.6259934,0.353598,0.009889726,0.002808967,0.00007897524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08872573,0.0003154497,0.9092363,0.00009191822,0.00003973177,0.00004205382,0.00003668514,0.0005493849,0.0009627946],"genre_scores_gemma":[0.6870319,0.0003775593,0.3111918,0.0000642291,0.00006577082,0.00007859404,0.00006823877,0.00007776511,0.001044297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001138022,"threshold_uncertainty_score":0.003444433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149777103869064,"score_gpt":0.2444535555597215,"score_spread":0.2294758451728151,"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."}}