{"id":"W3145571801","doi":"10.21810/jicw.v3i3.2752","title":"Detecting and Combating Deep Fakes","year":2021,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Presentation (obstetrics); Computer security; Political science; Internet privacy; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004507646,0.0009927294,0.0008833026,0.003274731,0.001312983,0.002921402,0.0005932164,0.002268187,0.003909918],"category_scores_gemma":[0.02244749,0.000448608,0.0003537168,0.0007284956,0.001003309,0.003258521,0.002239668,0.001927281,0.002861802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008140489,"about_ca_system_score_gemma":0.0007017242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008653781,"about_ca_topic_score_gemma":0.0010618,"domain_scores_codex":[0.9952235,0.001498557,0.0001821177,0.0004285648,0.002010523,0.0006568335],"domain_scores_gemma":[0.9833819,0.007051537,0.002553617,0.002411989,0.003658359,0.0009427589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006419137,0.0002991558,0.03728475,0.0005009994,0.0001483291,0.0007144049,0.001289066,0.005617574,0.03415529,0.01591743,0.06738894,0.8360422],"study_design_scores_gemma":[0.0001784417,0.003821697,0.08463884,0.001274275,0.0004478406,0.008828335,0.004978238,0.3420671,0.1976338,0.05991818,0.2957228,0.0004903276],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5526091,0.02006867,0.2978311,0.03150355,0.002514805,0.000912449,0.001576514,0.007096212,0.08588759],"genre_scores_gemma":[0.9263889,0.001938606,0.05626108,0.001771174,0.0005086914,0.0000763167,0.000464393,0.0001017796,0.01248914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004507646,"threshold_uncertainty_score":0.023839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02923402695146751,"score_gpt":0.2632571747400946,"score_spread":0.2340231477886271,"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."}}