{"id":"W4390859152","doi":"10.31234/osf.io/zhmy7","title":"Deepfake Detection in Super-Recognizers and Police Officers","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Software deployment; Computer science; Identity (music); Face (sociological concept); Observer (physics); Generative grammar; Artificial intelligence; Computer security; Sociology; Software engineering; Acoustics","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":[0.003263535,0.0005461887,0.0004206702,0.0005333974,0.0004152293,0.001261471,0.0003853196,0.0006203744,0.004141855],"category_scores_gemma":[0.02535995,0.0002991822,0.0002220538,0.0001757446,0.0007547764,0.001621518,0.001734009,0.0009417905,0.001099146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457921,"about_ca_system_score_gemma":0.0002721385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001232658,"about_ca_topic_score_gemma":0.001553025,"domain_scores_codex":[0.9982058,0.0006684392,0.00009225274,0.0004334534,0.0003280574,0.0002719946],"domain_scores_gemma":[0.9853167,0.008707918,0.002668261,0.001600479,0.0007834287,0.0009231095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003679002,0.001065388,0.6806552,0.0003463492,0.0002771274,0.0007753213,0.00783746,0.01899191,0.06938373,0.003634785,0.002655739,0.2106981],"study_design_scores_gemma":[0.00009939485,0.003606756,0.7159868,0.0001603093,0.000196745,0.002001,0.00881055,0.2076473,0.04192381,0.01366827,0.005678605,0.0002205051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934036,0.00005280527,0.004862184,0.00007936237,0.00001328819,0.00003141057,0.00005891117,0.00003252035,0.001466014],"genre_scores_gemma":[0.9975747,0.00002510756,0.001614313,0.00003939999,0.000007076504,0.00001296482,0.00006460388,0.000009655384,0.0006520922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004141855,"threshold_uncertainty_score":0.01725942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310242126730663,"score_gpt":0.2345513191656088,"score_spread":0.2214488978983022,"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."}}