{"id":"W1585805478","doi":"10.22042/isecure.2015.1.1.3","title":"Steganalysis of Embedding in Difference of Image Pixel Pairs by Neural Network","year":2009,"lang":"en","type":"article","venue":"Isecure.","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Steganalysis; Pixel; Steganography; Artificial intelligence; Histogram; Pattern recognition (psychology); Embedding; Image (mathematics); Perceptron; Computer science; Artificial neural network; Mathematics; Computer vision","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":[0.0001986112,0.0001281176,0.0002927992,0.0001515093,0.00003243935,0.00002234273,0.000691012,0.00005457505,0.000002276499],"category_scores_gemma":[0.00001556631,0.000113985,0.0001174206,0.0008131327,0.00005956994,0.0002749079,0.00008666934,0.0001296893,2.313719e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001087776,"about_ca_system_score_gemma":0.000009519969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001450882,"about_ca_topic_score_gemma":0.0000064109,"domain_scores_codex":[0.998881,0.00007388384,0.0003478098,0.0002476187,0.0001951086,0.0002545412],"domain_scores_gemma":[0.999239,0.00006535894,0.0001898105,0.0004163113,0.00004908584,0.00004049467],"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":[0.0002893373,0.001751384,0.1244319,0.0003365555,0.0002340118,0.0001337181,0.01013109,0.0222527,0.389078,0.09806252,0.008266518,0.3450323],"study_design_scores_gemma":[0.001512837,0.001281732,0.1049098,0.0007804664,0.00007880741,0.00001678753,0.0001397953,0.4642598,0.153617,0.2711461,0.0008445227,0.001412418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879397,0.0005849098,0.7102171,0.0002252944,0.00006644861,0.0001252124,0.000005552762,0.0001348454,0.0007009009],"genre_scores_gemma":[0.9538195,0.00004231293,0.04602195,0.00007887677,0.00001454248,0.000003231456,0.000002253641,0.000003867624,0.00001350607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6658797,"threshold_uncertainty_score":0.4648174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996437160689002,"score_gpt":0.2527039559223335,"score_spread":0.2447075187616445,"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."}}