{"id":"W4280506080","doi":"10.1007/s10489-022-03495-3","title":"Advanced defensive distillation with ensemble voting and noisy logits","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Computer science; Adversarial system; Robustness (evolution); Voting; Artificial intelligence; Artificial neural network; Vulnerability (computing); Machine learning; Logit; Distillation; Data mining; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.0002340549,0.0001463118,0.00014422,0.00007069532,0.0005019683,0.00007049038,0.0004811718,0.0000237698,0.00002084513],"category_scores_gemma":[0.00006550548,0.0001397929,0.00001729621,0.0004596434,0.00006653179,0.0002002988,0.0006888597,0.0003181109,0.00001434483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006615139,"about_ca_system_score_gemma":0.00003808857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001482957,"about_ca_topic_score_gemma":0.000006203905,"domain_scores_codex":[0.9987072,0.0000480123,0.0001775956,0.0004951191,0.0003148556,0.0002572141],"domain_scores_gemma":[0.9992077,0.0002393675,0.0001508029,0.000290822,0.00005272735,0.00005855815],"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":[0.00003468043,0.00001314721,0.0007026719,0.000008464823,0.000008728479,0.00001421498,0.001318958,0.4973738,0.0009970936,0.3111445,0.00001285408,0.1883709],"study_design_scores_gemma":[0.0004570261,0.0004490642,0.002653918,0.00003247121,0.0000234869,0.0001841664,0.002605713,0.9554105,0.0114785,0.02199094,0.003735069,0.0009791445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03503375,0.00006317239,0.9603364,0.0001572432,0.0001258909,0.0002166262,7.536279e-7,0.0001567725,0.003909418],"genre_scores_gemma":[0.9063162,0.000004496847,0.09331957,0.0002158078,0.00002515105,0.00004486848,0.000003050693,0.00001291385,0.00005793433],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8712825,"threshold_uncertainty_score":0.5700591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004385080624384,"score_gpt":0.2261156491019867,"score_spread":0.2160717982957429,"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."}}