{"id":"W2375822619","doi":"","title":"Study on dependencies between wavelet coefficients for image steganalysis and its application","year":2010,"lang":"en","type":"article","venue":"Jisuanji yingyong yanjiu","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Alberta Paraplegic Foundation","funders":"","keywords":"Steganalysis; Computer science; Steganography; Wavelet; Artificial intelligence; Embedding; Pattern recognition (psychology); Image (mathematics); Uncompressed video; 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":[0.0006356761,0.0002409704,0.0002984129,0.0003318759,0.0004171155,0.0002225753,0.0008212623,0.0001123988,0.000001083779],"category_scores_gemma":[0.0001068647,0.0002135546,0.0000963378,0.0006322842,0.00005397289,0.0004878982,0.0002430584,0.0002749423,0.00000880108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950069,"about_ca_system_score_gemma":0.00002055035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001000996,"about_ca_topic_score_gemma":0.00001766113,"domain_scores_codex":[0.9982213,0.00005460199,0.0003170324,0.0006900794,0.0003574073,0.0003595447],"domain_scores_gemma":[0.9985372,0.0002458455,0.0001739445,0.0007099562,0.0002159869,0.0001171093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000151419,0.002694927,0.3926203,0.0003065213,0.0007466982,0.00007820749,0.01055743,0.00006210276,0.1533789,0.07630274,0.000513889,0.3625868],"study_design_scores_gemma":[0.004294088,0.002522713,0.6535547,0.0001312238,0.0004813451,0.00004471889,0.001232542,0.03903658,0.2551958,0.03350838,0.007157902,0.002839933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5645133,0.00001074372,0.4340974,0.0001143294,0.00008245052,0.0006946158,0.00001166732,0.0003065023,0.0001689831],"genre_scores_gemma":[0.9704458,0.000003999311,0.02912312,0.00006904533,0.00009271229,0.0001755995,0.00001307426,0.00002067478,0.0000559895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4059325,"threshold_uncertainty_score":0.8708502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024065761359568,"score_gpt":0.3088432798011399,"score_spread":0.2847775184415719,"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."}}