{"id":"W2963693747","doi":"10.1109/cvpr.2019.00445","title":"Decoupling direction and norm for efficient gradient-based L2 adversarial attacks and defenses","year":2019,"lang":"","type":"article","venue":"Espace ÉTS (ETS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":252,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"MNIST database; Adversarial system; Norm (philosophy); Computer science; Decoupling (probability); Robustness (evolution); Artificial intelligence; Perturbation (astronomy); Algorithm; Pattern recognition (psychology); Machine learning; Mathematical optimization; Deep learning; Mathematics; Engineering","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":[0.001355408,0.0006568827,0.0007076431,0.0004174993,0.0009449944,0.0006622066,0.0005556419,0.00035162,0.0000531326],"category_scores_gemma":[0.0007632319,0.0006973385,0.0002115901,0.0006218407,0.0002703539,0.0005458175,0.0006287777,0.0006140289,0.00005266674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533394,"about_ca_system_score_gemma":0.0002510228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001517928,"about_ca_topic_score_gemma":0.0001087088,"domain_scores_codex":[0.9958451,0.0002049413,0.0005722499,0.001715673,0.0006237185,0.001038335],"domain_scores_gemma":[0.9965934,0.001417053,0.0005353743,0.0008175133,0.0002504799,0.0003861503],"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":[0.001207814,0.0002704711,0.03961799,0.0005862362,0.0001614723,0.00002268986,0.005988119,0.8900856,0.001324517,0.006131987,0.0002359805,0.05436716],"study_design_scores_gemma":[0.004544839,0.000605893,0.007604165,0.0002929379,0.0001451327,0.00002636128,0.0003459894,0.9791414,0.0007263886,0.00009112641,0.005668447,0.0008072854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5402941,0.0007394713,0.451756,0.001034965,0.00463619,0.001143891,0.00001081472,0.0001471611,0.0002374647],"genre_scores_gemma":[0.9720788,0.00005483267,0.02627097,0.0001976909,0.000544025,0.00003966067,0.000009838365,0.0000773611,0.000726855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4317847,"threshold_uncertainty_score":0.9995478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118041105150784,"score_gpt":0.2603883496451301,"score_spread":0.2492079385936223,"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."}}