{"id":"W2392459981","doi":"","title":"A JPEG Steganalysis Algorithm based on Kernel Fisher Discriminant","year":2005,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Steganalysis; Steganography; JPEG; Computer science; Fisher kernel; Kernel Fisher discriminant analysis; Pattern recognition (psychology); Kernel (algebra); Linear discriminant analysis; Discrete cosine transform; Artificial intelligence; Histogram; Image (mathematics); Feature vector; Feature (linguistics); Information hiding; Kernel method; Algorithm; Support vector machine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004513483,0.0004466048,0.0006902194,0.0009021436,0.0002804619,0.0003000108,0.0004121951,0.0005187619,0.0009713221],"category_scores_gemma":[0.000723439,0.0002128378,0.0003505188,0.0005694819,0.0002853141,0.0007322685,0.0002959247,0.0005888498,0.0006235375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003411382,"about_ca_system_score_gemma":0.0005225271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002395406,"about_ca_topic_score_gemma":0.002893757,"domain_scores_codex":[0.9997813,0.00002428652,0.00001018358,0.00004424536,0.0001189847,0.00002104074],"domain_scores_gemma":[0.9997858,0.00004377544,0.00002023551,0.00001872001,0.0001205335,0.00001090121],"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":[0.000234413,0.0001348799,0.002398033,0.0001165458,0.00008249896,0.0001012893,0.00006773591,0.02732506,0.1116331,0.007082619,0.003592462,0.8472313],"study_design_scores_gemma":[0.00007505502,0.0002436835,0.005616154,0.00002153605,0.00006079417,0.0009806564,0.00003691755,0.8811083,0.09830857,0.003117868,0.0103582,0.00007229939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02546186,0.0003984411,0.971666,0.0001135423,0.0000841319,0.00007636517,0.00006055836,0.001074984,0.001064171],"genre_scores_gemma":[0.2198686,0.0003938053,0.7741964,0.00008289421,0.00004873518,0.00009613678,0.0002791626,0.00008202072,0.004952293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002395406,"threshold_uncertainty_score":0.004762888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008496255354822397,"score_gpt":0.2424643907683283,"score_spread":0.2339681354135059,"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."}}