{"id":"W4312438979","doi":"10.1109/tdsc.2022.3217569","title":"Cover Reproducible Steganography via Deep Generative Models","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Steganography; Computer science; Steganalysis; Cover (algebra); Theoretical computer science; Steganography tools; Decoding methods; Cryptography; Embedding; Artificial intelligence; Algorithm; Pattern recognition (psychology); Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004990282,0.0007108153,0.0006377444,0.0004391299,0.0002465538,0.0007069248,0.0006558466,0.0008863899,0.001465126],"category_scores_gemma":[0.001626068,0.0005150491,0.0009526564,0.0003320365,0.0009539947,0.00093384,0.0009795103,0.001258428,0.0003792522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007937205,"about_ca_system_score_gemma":0.0005448724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003432286,"about_ca_topic_score_gemma":0.004898426,"domain_scores_codex":[0.9997737,0.00007848965,0.00000834535,0.00004811388,0.00005510353,0.00003627749],"domain_scores_gemma":[0.9991062,0.0006564544,0.00008161282,0.00007249938,0.00005362753,0.00002966653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004666445,0.00001966918,0.000422312,0.00004363029,0.00003754507,0.00009058564,0.00006716209,0.9401487,0.003301193,0.02991305,0.0005321368,0.02537739],"study_design_scores_gemma":[0.000002219643,0.000005294489,0.00003165857,0.00000234001,0.000003819763,0.00001112985,0.000001976735,0.9927344,0.000364828,0.00668873,0.000150906,0.000002661282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02899517,0.0005137015,0.9669257,0.0002876132,0.00002750077,0.00002317951,0.00008249739,0.0004296568,0.002715004],"genre_scores_gemma":[0.891949,0.0008101139,0.09945696,0.0001967607,0.00006401605,0.0001267188,0.0002805479,0.0001649322,0.006951017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003432286,"threshold_uncertainty_score":0.006824613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634097675994247,"score_gpt":0.2320379466060755,"score_spread":0.215696969846133,"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."}}