{"id":"W4394825026","doi":"10.5121/ijaia.2024.15205","title":"Immunizing Image Classifiers Against Localized Adversary Attack","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Convolutional neural network; Computer science; Adversary; Deep learning; Artificial intelligence; Vulnerability (computing); Machine learning; Convolution (computer science); Image (mathematics); Scale (ratio); Deep neural networks; Artificial neural network; Pattern recognition (psychology); Computer security; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001435892,0.001000267,0.0009752018,0.0005274111,0.000407235,0.0006915309,0.0009960429,0.001262932,0.00119171],"category_scores_gemma":[0.006200925,0.000364693,0.0006208517,0.0002401027,0.001210059,0.002139528,0.002774644,0.001611725,0.0006588456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006842929,"about_ca_system_score_gemma":0.0006278738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008173555,"about_ca_topic_score_gemma":0.0008157056,"domain_scores_codex":[0.9991393,0.0001934971,0.00003651506,0.0001712822,0.0002747461,0.0001845545],"domain_scores_gemma":[0.997698,0.0009758488,0.000340622,0.000659288,0.0002168934,0.0001092855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004544867,0.0001542396,0.004206792,0.0001232788,0.0001592625,0.000249617,0.0001612563,0.7221543,0.06424871,0.01766317,0.004371544,0.1860534],"study_design_scores_gemma":[0.000006155566,0.0001156256,0.0003098202,0.00001130196,0.00001418557,0.00006997454,0.00001881251,0.9804684,0.01444124,0.003761936,0.0007737406,0.000008804821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2536776,0.0009245627,0.7353367,0.0009331066,0.0002160535,0.0001172426,0.0001038227,0.002517062,0.006173829],"genre_scores_gemma":[0.9649066,0.000204679,0.03253434,0.0002635055,0.00006653973,0.00004231092,0.0001070408,0.00005916529,0.001815952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001435892,"threshold_uncertainty_score":0.007593811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972670245325821,"score_gpt":0.356184689767243,"score_spread":0.3164579873139848,"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."}}