{"id":"W4205668919","doi":"10.1109/tnse.2021.3137829","title":"High-Capacity Steganography Using Object Addition-Based Cover Enhancement for Secure Communication in Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Steganography; Cover (algebra); Embedding; Computer science; Object (grammar); Image (mathematics); Theoretical computer science; Artificial intelligence; Steganography tools; Computer vision; Pattern recognition (psychology); Data mining; Engineering","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":[0.0005167171,0.0001498775,0.000152751,0.0002536086,0.0004536319,0.000165207,0.0003475889,0.00006664949,0.000002758597],"category_scores_gemma":[0.000006064509,0.0001598305,0.00006417849,0.001911474,0.0001072861,0.0006736049,0.000006746742,0.0002231239,1.788094e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008225476,"about_ca_system_score_gemma":0.0000895739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009615738,"about_ca_topic_score_gemma":0.00001370135,"domain_scores_codex":[0.9987919,0.00003067824,0.0002055899,0.0003630164,0.0002227246,0.0003861343],"domain_scores_gemma":[0.9991673,0.0001674479,0.00004970132,0.0004070207,0.0001362307,0.00007235427],"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":[0.000007244401,0.00003899357,0.00001732095,0.0000160271,0.000006431078,0.000001358288,0.0000689002,0.992539,0.001885014,0.001474717,0.0000200334,0.003924961],"study_design_scores_gemma":[0.0002556908,0.00005626862,0.0001277946,0.0002272361,0.000007517984,0.000004180865,0.000005798928,0.9662868,0.03187543,0.0006480034,0.0003033108,0.0002019061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02560112,0.0001465895,0.9734372,0.00006455163,0.0003955023,0.0001995181,0.000005593244,0.0001355501,0.00001436526],"genre_scores_gemma":[0.7722761,0.0001009497,0.2273919,0.0001313542,0.00002490783,0.0000636398,0.000002342648,0.000006903591,0.000001913603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.746675,"threshold_uncertainty_score":0.6517698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379116433955203,"score_gpt":0.2245306943632904,"score_spread":0.2107395300237384,"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."}}