{"id":"W4404704192","doi":"10.1561/116.20240038","title":"A Comprehensive Survey of Digital Image Steganography and Steganalysis","year":2024,"lang":"en","type":"article","venue":"APSIPA Transactions on Signal and Information Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Steganalysis; Steganography; Digital image; Computer science; Artificial intelligence; Steganography tools; Image (mathematics); Computer vision; Computer security; Image processing","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":[0.0001383487,0.0001264561,0.0001548568,0.0005509057,0.0001830685,0.0006414412,0.0001243865,0.00004685931,0.000003648465],"category_scores_gemma":[0.000002764215,0.0001046569,0.00005675333,0.0009841879,0.0001198117,0.008383696,0.00001008882,0.0001345304,0.000001713675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006285152,"about_ca_system_score_gemma":0.00002917841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001565342,"about_ca_topic_score_gemma":0.000001344881,"domain_scores_codex":[0.9992176,0.00002653488,0.0003055882,0.0001531861,0.0001768807,0.0001201721],"domain_scores_gemma":[0.9994826,0.0001017998,0.00007653044,0.0001063777,0.0001798477,0.00005289282],"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":[0.00003675003,0.00003458407,0.0002795416,0.0005144422,0.00008068986,0.000001943541,0.002249015,0.0001256797,0.0004223662,0.001647342,0.00001547497,0.9945922],"study_design_scores_gemma":[0.002016929,0.001823426,0.03981603,0.002455592,0.0003317877,0.0003034348,0.002234478,0.8145928,0.07902025,0.03672659,0.01816813,0.002510491],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01828994,0.0003694058,0.9804235,0.00008592199,0.00002910523,0.00008559912,0.00003995625,0.0002014079,0.0004751522],"genre_scores_gemma":[0.991897,0.0001332137,0.007857799,0.00007326136,0.000002938432,0.000009287635,0.00001376586,0.000003997876,0.000008703099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9920817,"threshold_uncertainty_score":0.6185432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460887602743838,"score_gpt":0.2467037970387301,"score_spread":0.2320949210112917,"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."}}