{"id":"W2964434332","doi":"","title":"Low Frequency Adversarial Perturbation","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Black box; Image (mathematics); Perturbation (astronomy); Cloud computing; Frequency domain; Transformation (genetics); Artificial intelligence; Theoretical computer science; Computer vision; Algorithm","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"],"consensus_categories":[],"category_scores_codex":[0.001015607,0.0002474895,0.0002405737,0.0003041457,0.0002718824,0.0002005327,0.001431401,0.000162135,0.0002968848],"category_scores_gemma":[0.001162215,0.0002488481,0.00008517656,0.001200056,0.0003214541,0.0007760671,0.0002977522,0.0004318923,0.0007066485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002599062,"about_ca_system_score_gemma":0.000177719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005546663,"about_ca_topic_score_gemma":0.0007088163,"domain_scores_codex":[0.997353,0.0002834119,0.0006283156,0.0007131933,0.000455295,0.0005668411],"domain_scores_gemma":[0.9984267,0.0003210186,0.0001744377,0.0007079316,0.0002551172,0.0001148269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003891847,0.0001231429,0.0002721839,0.000008710063,0.000009010066,0.0000303982,0.003998017,0.06203754,0.001701545,0.7074978,0.00008341512,0.2241994],"study_design_scores_gemma":[0.0000572698,0.0001647016,0.0001529857,0.00004770514,0.000003352058,0.000005911546,0.000256718,0.7077595,0.008261301,0.2825394,0.0004380918,0.0003130693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01452699,0.00001937539,0.9744436,0.001626645,0.002427648,0.0002599838,0.000001663358,0.0002458033,0.006448349],"genre_scores_gemma":[0.9653844,0.000006135111,0.03312543,0.0004588249,0.0008611937,0.00001923331,0.000005335107,0.00001586676,0.0001236403],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9508573,"threshold_uncertainty_score":0.9999964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02908829838875739,"score_gpt":0.2972562858240615,"score_spread":0.2681679874353041,"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."}}