{"id":"W4387735260","doi":"10.1145/3627817","title":"It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Machine learning; Artificial intelligence; Mistake; Vulnerability (computing); Adversarial machine learning; Computer security; Threat model; Data science","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":[0.002777453,0.001176264,0.00141346,0.003103257,0.0006921148,0.002540717,0.00143072,0.001995488,0.00602043],"category_scores_gemma":[0.007758799,0.0008330799,0.0008550565,0.004093381,0.002025137,0.005196302,0.001719366,0.002818528,0.0029753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137032,"about_ca_system_score_gemma":0.001223981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113663,"about_ca_topic_score_gemma":0.001065308,"domain_scores_codex":[0.998259,0.0004756743,0.0001284006,0.0002298917,0.0007935141,0.0001134923],"domain_scores_gemma":[0.9894044,0.008475471,0.0003824902,0.0005319312,0.001072663,0.0001330081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005438831,0.0001011516,0.001221323,0.006208421,0.0001090368,0.0001793036,0.0002183698,0.00882746,0.0007668216,0.1367534,0.03664757,0.8089128],"study_design_scores_gemma":[0.00001609911,0.000241353,0.002128908,0.007599773,0.0001299181,0.001720514,0.0003771353,0.009349279,0.001818808,0.1016893,0.8748249,0.0001040024],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001435851,0.9460791,0.02394959,0.00270883,0.000656481,0.00005157525,0.00007576784,0.0001105061,0.0249324],"genre_scores_gemma":[0.01815142,0.9690115,0.006583766,0.001089185,0.001235381,0.00006232372,0.000122339,0.00005078629,0.00369312],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00602043,"threshold_uncertainty_score":0.02014035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2351525607181049,"score_gpt":0.4152004103574418,"score_spread":0.1800478496393369,"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."}}