{"id":"W4285411630","doi":"10.5220/0011307200003283","title":"Resilience of GANs against Adversarial Attacks","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adversarial system; Resilience (materials science); Computer science; Computer security; Artificial intelligence; Physics","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":[0.001944981,0.001175935,0.0009093137,0.0006829464,0.0003656911,0.001009875,0.00110475,0.001278771,0.00267421],"category_scores_gemma":[0.01395892,0.0005107223,0.0005732753,0.0003681373,0.001279125,0.001751197,0.002542951,0.002350398,0.0006281455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007788909,"about_ca_system_score_gemma":0.0005869379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128984,"about_ca_topic_score_gemma":0.0008543439,"domain_scores_codex":[0.9989599,0.0003386118,0.00004099957,0.0002003492,0.0002699209,0.00019021],"domain_scores_gemma":[0.9942192,0.003690734,0.000404569,0.001013021,0.0004432309,0.0002291684],"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":[0.000168342,0.00003630614,0.000874071,0.00007295646,0.0000755135,0.0001460445,0.00006264028,0.9157759,0.006469687,0.0507599,0.002907437,0.02265107],"study_design_scores_gemma":[0.000004974051,0.0000303589,0.0002246912,0.00001358185,0.000007918035,0.00005997451,0.00001082349,0.9730909,0.001202605,0.0247921,0.0005551326,0.000006987602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1109862,0.00125861,0.86747,0.001506929,0.0003263592,0.00008175654,0.0003867616,0.002381623,0.01560174],"genre_scores_gemma":[0.9744951,0.0004337328,0.02023108,0.0002531852,0.00009620355,0.00005386509,0.0002565005,0.0002222699,0.003958101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00267421,"threshold_uncertainty_score":0.01028615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009104756857500465,"score_gpt":0.2465511924899267,"score_spread":0.2374464356324262,"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."}}