{"id":"W4386018806","doi":"10.1016/j.compbiomed.2023.107344","title":"Steganography for medical record image","year":2023,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Steganography; Artificial intelligence; Computer vision; Distortion (music); Watermark; Image (mathematics); Decoding methods; Computer network; Telecommunications","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":[0.0003149102,0.0002456907,0.0002454247,0.000707068,0.0002027299,0.0005359069,0.0002589214,0.0005906876,0.00881607],"category_scores_gemma":[0.001675053,0.0001206986,0.0002746808,0.0004753998,0.0002653221,0.000766295,0.0004674648,0.0005062869,0.004482917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002614754,"about_ca_system_score_gemma":0.0002831454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004938419,"about_ca_topic_score_gemma":0.0005716289,"domain_scores_codex":[0.9997167,0.00006484801,0.00001688151,0.0000400732,0.0001305397,0.00003097509],"domain_scores_gemma":[0.9993789,0.0002489859,0.00004372275,0.0001535919,0.0001551459,0.00001965849],"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":[0.0004025252,0.00008576421,0.001086227,0.0008374981,0.00006004135,0.0006068031,0.0002834056,0.004731014,0.1988553,0.049049,0.02039021,0.7236122],"study_design_scores_gemma":[0.0001031164,0.0007560859,0.01036226,0.0004549771,0.0002198764,0.008809046,0.0002873341,0.2383406,0.4906411,0.02951906,0.2204234,0.00008322606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07654447,0.006309613,0.8687724,0.004217211,0.0009590316,0.0002924102,0.001339053,0.003997141,0.03756861],"genre_scores_gemma":[0.4945314,0.006953942,0.4199696,0.0006887118,0.0006565426,0.0001754498,0.001778991,0.0003338629,0.07491145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00881607,"threshold_uncertainty_score":0.02949268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834063920821603,"score_gpt":0.3343279183614275,"score_spread":0.3159872791532115,"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."}}