{"id":"W3192896178","doi":"10.1109/jiot.2021.3103779","title":"Coverless Information Hiding Based on Probability Graph Learning for Secure Communication in IoT Environment","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Computer science; Information hiding; Cover (algebra); Steganography; Secure communication; Graph; Node (physics); Steganalysis; Internet of Things; Scheme (mathematics); Theoretical computer science; Computer network; Computer security; Artificial intelligence; Encryption; Embedding","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":[0.0008965307,0.00009769767,0.0001471445,0.0001951525,0.00008689817,0.0001367908,0.0005445833,0.00006465429,0.000003452499],"category_scores_gemma":[0.00008486137,0.00009235442,0.0001166396,0.0001374111,0.00003591749,0.0009417724,0.00008011918,0.0004416712,5.625739e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014467,"about_ca_system_score_gemma":0.00003359738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000757576,"about_ca_topic_score_gemma":0.000001095427,"domain_scores_codex":[0.9989321,0.0001610808,0.0004114109,0.0001217633,0.000226781,0.0001468915],"domain_scores_gemma":[0.9990868,0.0001560051,0.000360552,0.0002623711,0.00009876047,0.00003553976],"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":[0.001129289,0.001618592,0.05535237,0.001105986,0.0002032148,0.00006059317,0.05853655,0.4644054,0.02308209,0.06217486,0.001724354,0.3306067],"study_design_scores_gemma":[0.001471366,0.0006040767,0.001881442,0.001568314,0.00001291793,0.00008197725,0.0001776231,0.6141872,0.2470222,0.1264895,0.006116625,0.000386774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1061634,0.00003717302,0.8929793,0.0003555062,0.0001212307,0.0001249217,6.882873e-7,0.00003776144,0.0001799725],"genre_scores_gemma":[0.8378872,0.00003089668,0.1618559,0.000191256,0.00000853508,0.00001132778,0.000003482314,0.000003726277,0.000007775183],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7317237,"threshold_uncertainty_score":0.3766104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144586742229911,"score_gpt":0.2394108511981395,"score_spread":0.2249521769751484,"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."}}