{"id":"W4310415410","doi":"10.48550/arxiv.2211.14667","title":"Deep Fake Detection, Deterrence and Response: Challenges and Opportunities","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cybercrime; Hacker; Computer security; Deterrence theory; Internet privacy; Hoax; Software deployment; Identification (biology); Computer science; Business; Political science; Law; World Wide Web; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004379727,0.0002973278,0.0002716965,0.0004506219,0.0003260246,0.00009371092,0.000827169,0.0001906636,0.00001564855],"category_scores_gemma":[0.00006910994,0.0003818903,0.00006548347,0.0001860383,0.0002091155,0.000530166,0.003419513,0.0005977294,0.000001750284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816393,"about_ca_system_score_gemma":0.00009375529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003131671,"about_ca_topic_score_gemma":0.0001209282,"domain_scores_codex":[0.9979771,0.0003566713,0.0001717501,0.001134,0.0001002152,0.0002603118],"domain_scores_gemma":[0.998373,0.0002431273,0.0002211098,0.0008913592,0.0001076951,0.0001636733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007433714,0.0001458588,0.0004054579,0.000426095,0.0002090664,0.002232502,0.005785512,0.0134471,0.0007233456,0.1911695,0.00004567457,0.7846665],"study_design_scores_gemma":[0.001381722,0.001637153,0.01548277,0.0002552722,0.0001711903,0.0005224306,0.005206882,0.4595047,0.005180551,0.4711007,0.03611205,0.003444591],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1306322,0.002359715,0.8647921,0.0002502535,0.0003191124,0.0002678594,0.000005293122,0.000850061,0.0005233885],"genre_scores_gemma":[0.9814618,0.0144849,0.003334901,0.00008392595,0.00002142617,0.000009101404,0.000001577433,0.00001903547,0.0005833439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8614572,"threshold_uncertainty_score":0.9998633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1320925092406114,"score_gpt":0.2086241897741513,"score_spread":0.07653168053353987,"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."}}