{"id":"W2095021826","doi":"10.1007/s11042-011-0975-y","title":"An adaptive LSB matching steganography based on octonary complexity measure","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Least significant bit; Steganalysis; Computer science; Steganography; Embedding; Pixel; Algorithm; Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics","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":[0.0002527501,0.0001804922,0.000152729,0.0001433605,0.000393159,0.0001179531,0.0004735666,0.00007538544,0.000004161805],"category_scores_gemma":[0.0000045487,0.0001592278,0.00007041755,0.0003711063,0.0001418369,0.0008693004,0.00006777034,0.0001910618,0.000007448889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001400503,"about_ca_system_score_gemma":0.00001470535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001405104,"about_ca_topic_score_gemma":0.000002824279,"domain_scores_codex":[0.9988781,0.00007947238,0.0001728435,0.0003424805,0.0002098305,0.0003172745],"domain_scores_gemma":[0.9988548,0.0001622499,0.00008185957,0.000622976,0.00005545098,0.0002226603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006120287,0.001240666,0.01260743,0.00003424594,0.00003747229,0.000002307238,0.001692666,0.0002397923,0.004069383,0.2733975,0.000181392,0.7064359],"study_design_scores_gemma":[0.002239812,0.001142838,0.3663777,0.0002668914,0.00009187144,0.00003432616,0.0004216762,0.3867005,0.02185123,0.1665775,0.05147262,0.002823155],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004866033,0.0001258886,0.9922091,0.0002097312,0.00004932681,0.0005273308,0.00005830068,0.0005164135,0.001437888],"genre_scores_gemma":[0.6807059,0.000008593341,0.3185464,0.0003133275,0.00009115904,0.0002912892,0.00003204174,0.000008864345,0.000002469184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7036127,"threshold_uncertainty_score":0.6493121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04961857296362435,"score_gpt":0.2858580914526628,"score_spread":0.2362395184890384,"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."}}