{"id":"W4390189529","doi":"10.1109/milcom58377.2023.10356292","title":"Constant Scaling Asymptotics of Communication Bounds in Covert Channels Against Selective Adversary","year":2023,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Tsinghua Shenzhen International Graduate School","keywords":"Code word; Binary number; Transmitter; Covert; Adversary; Mathematics; Constraint (computer-aided design); Adversary model; Computer science; Constant (computer programming); Upper and lower bounds; Distribution (mathematics); Simple (philosophy); Square root; Algorithm; Decoding methods; Computer network; Statistics; Arithmetic; Mathematical analysis","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":[0.0003617495,0.0001078833,0.0001988475,0.0002924529,0.000041433,0.00001764193,0.0003668305,0.00009964003,0.000008742109],"category_scores_gemma":[0.00005058149,0.0001254547,0.00003837173,0.0008386041,0.00008015573,0.0001632424,0.0001438264,0.0002324859,0.00001639583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362704,"about_ca_system_score_gemma":0.00002743126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005712429,"about_ca_topic_score_gemma":0.00008305987,"domain_scores_codex":[0.9991812,0.00006886577,0.0003418761,0.00009314652,0.0001393772,0.0001755191],"domain_scores_gemma":[0.9990499,0.0002327571,0.00004710868,0.0005528508,0.00008663654,0.00003072751],"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":[0.00008723558,0.0005603372,0.02787781,0.0007886676,0.0004969056,0.00001965072,0.03297484,0.6223872,0.1048206,0.1410567,0.02798289,0.04094711],"study_design_scores_gemma":[0.0006344221,0.00003328197,0.00396318,0.0003954259,0.00001015515,0.000002347291,0.001771909,0.8563832,0.1284228,0.005368104,0.002576271,0.0004388971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9019417,0.0007349098,0.009698415,0.0003253477,0.0001279247,0.0005629106,0.00002730723,0.002765709,0.08381579],"genre_scores_gemma":[0.9952517,0.002087276,0.00245993,0.00004745682,0.000005576003,0.00002867315,0.00006133435,0.0000271405,0.00003091369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.233996,"threshold_uncertainty_score":0.5115895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424044078501589,"score_gpt":0.248821991762997,"score_spread":0.2345815509779811,"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."}}