{"id":"W4408146758","doi":"10.1109/icmla61862.2024.00234","title":"Securing 3D Deep Learning Models: Simple and Effective Defense Against Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Adversarial system; Computer science; Simple (philosophy); Deep learning; Computer security; Artificial intelligence; Epistemology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000630799,0.0002796756,0.000256605,0.0002137697,0.0003670807,0.0005520989,0.0004578767,0.0001302506,0.00002511708],"category_scores_gemma":[0.0002191443,0.0002593813,0.00009407869,0.0004578111,0.00006971445,0.001469412,0.0008855406,0.0007885273,0.00005764166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001162003,"about_ca_system_score_gemma":0.00004940216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006352644,"about_ca_topic_score_gemma":0.00001710989,"domain_scores_codex":[0.9978733,0.0002576633,0.0002470501,0.000816958,0.0003303581,0.0004746707],"domain_scores_gemma":[0.9986634,0.0007459014,0.00005678846,0.0003306534,0.00005465701,0.000148611],"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":[0.00001632203,0.0000115501,0.0003128438,0.00006924968,0.0000683295,0.0001460501,0.00415033,0.6965081,0.0001198867,0.06017376,0.00006034403,0.2383632],"study_design_scores_gemma":[0.0003886611,0.00007200043,0.000101539,0.00005481453,0.00001910572,0.00002840107,0.0001739741,0.9892297,0.00007599407,0.006574434,0.002958564,0.0003227697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0319288,0.0005475288,0.9469366,0.0001366735,0.0006948789,0.0002626172,6.058267e-7,0.001125067,0.01836724],"genre_scores_gemma":[0.9558699,0.00004304949,0.04338166,0.0001382769,0.0002953479,0.00002216729,0.000004748748,0.00003743042,0.0002074914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.923941,"threshold_uncertainty_score":0.9999858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119266540343275,"score_gpt":0.2518388314573767,"score_spread":0.2399121774230492,"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."}}