{"id":"W4392906114","doi":"10.32920/25412851","title":"Security of Generative Adversarial Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"MNIST database; Discriminator; Adversarial system; Computer science; Generative grammar; Attack surface; Computer security; Generative adversarial network; Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Telecommunications","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":[0.005266112,0.001174746,0.001071923,0.0007926138,0.000738252,0.001927798,0.001396214,0.001888753,0.002561504],"category_scores_gemma":[0.01474353,0.0005759883,0.000972909,0.0003836347,0.003007079,0.002510404,0.003375863,0.003691716,0.0007556796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613396,"about_ca_system_score_gemma":0.001009093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150992,"about_ca_topic_score_gemma":0.000973983,"domain_scores_codex":[0.9967061,0.001575462,0.0001278972,0.0004931538,0.0007878793,0.0003096006],"domain_scores_gemma":[0.985609,0.01053115,0.0007349027,0.00219666,0.0007034253,0.0002248076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002156929,0.00003993664,0.001506811,0.0001065619,0.0001076442,0.0001516245,0.0001191514,0.8184814,0.004328938,0.1439003,0.002505419,0.02853645],"study_design_scores_gemma":[0.000008995758,0.00003317186,0.0001534975,0.00002480017,0.000009639486,0.00005961496,0.00001182479,0.9387199,0.001490647,0.05847092,0.001004258,0.0000128204],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05365332,0.001265531,0.9252861,0.002461568,0.000202471,0.0001166129,0.000265037,0.001192724,0.01555666],"genre_scores_gemma":[0.9472967,0.0007327338,0.04634758,0.0006723309,0.0001123778,0.0001187675,0.000280778,0.0001649166,0.004273856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005266112,"threshold_uncertainty_score":0.02785021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168006359099469,"score_gpt":0.2705876212344714,"score_spread":0.2589075576434767,"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."}}