{"id":"W4388621503","doi":"10.1007/978-981-99-8248-6_24","title":"Combating Computer Vision-Based Aim Assist Tools in Competitive Online Games","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Adversarial system; Domain (mathematical analysis); Set (abstract data type); Human–computer interaction; Artificial intelligence; Object (grammar); Cheating; Black box; Computer game; Competitor analysis; Computer vision; Computer security; Multimedia; Programming language","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":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002150043,0.001023353,0.001249737,0.002076407,0.0004352804,0.001254234,0.005352244,0.0005665431,0.00003330513],"category_scores_gemma":[0.0006099304,0.001005076,0.0002698339,0.001965306,0.001116519,0.001073949,0.003398033,0.002745142,0.0001021442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007569436,"about_ca_system_score_gemma":0.001004115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008784766,"about_ca_topic_score_gemma":0.0005647482,"domain_scores_codex":[0.9925889,0.0002432503,0.001236476,0.002777018,0.001881675,0.001272711],"domain_scores_gemma":[0.9921181,0.004676518,0.000713125,0.001835575,0.000395261,0.0002614724],"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":[0.0000110275,0.00006575415,0.0003494629,0.00005742414,0.00001093171,0.0003989721,0.0003710834,0.5325182,0.00001317312,0.03233562,0.00001278039,0.4338556],"study_design_scores_gemma":[0.0007921192,0.0003077699,0.003421611,0.001857326,0.000008631962,0.00002744576,0.000001042691,0.9655475,0.00005427737,0.02620944,0.0007064659,0.001066344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001716927,0.00009659011,0.9910286,0.002317955,0.003408044,0.0006393865,0.00002752274,0.0005885879,0.001721639],"genre_scores_gemma":[0.1592016,0.00001734555,0.8355148,0.003619867,0.001131787,0.00001965479,0.00008711276,0.000142183,0.0002656717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4330293,"threshold_uncertainty_score":0.9997826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485131460734083,"score_gpt":0.2910374366453593,"score_spread":0.2661861220380184,"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."}}