{"id":"W4380089110","doi":"10.2139/ssrn.4474509","title":"Calibration Attack: Adversarial Attacks Against Model Calibration","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Queen's University","funders":"","keywords":"Adversarial system; Calibration; Computer science; Computer security; Artificial intelligence; Mathematics; Statistics","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":[0.00349903,0.001568057,0.001300684,0.0008894586,0.0007172549,0.001500358,0.001633867,0.003998104,0.004801108],"category_scores_gemma":[0.02321528,0.0006845598,0.001177025,0.0008726086,0.002431453,0.003613939,0.007157981,0.00535501,0.001446811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006458455,"about_ca_system_score_gemma":0.0006996288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003601604,"about_ca_topic_score_gemma":0.000215786,"domain_scores_codex":[0.9959294,0.001577289,0.0001437772,0.0007292888,0.001237235,0.0003830409],"domain_scores_gemma":[0.9898452,0.005273713,0.0007797467,0.003418338,0.0004671286,0.0002158884],"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":[0.0007103709,0.0001456213,0.001530064,0.0001714929,0.000271812,0.0005459661,0.0001930553,0.6204017,0.01941148,0.2191623,0.01498681,0.1224694],"study_design_scores_gemma":[0.00002618772,0.0000721738,0.0002258969,0.00002438875,0.00001935471,0.0002456982,0.00002033454,0.8982313,0.00702143,0.09177983,0.002310373,0.00002309421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01916854,0.0003146767,0.9697223,0.001195041,0.0002070469,0.00007219743,0.0001486284,0.001679719,0.007491824],"genre_scores_gemma":[0.8865713,0.0003807946,0.1041983,0.001180661,0.0002509032,0.000127362,0.00039218,0.0004697854,0.006428704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004801108,"threshold_uncertainty_score":0.01850486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03142698594566107,"score_gpt":0.2950429123845449,"score_spread":0.2636159264388838,"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."}}