{"id":"https://openalex.org/W4392908096","doi":"https://doi.org/10.1109/tetci.2024.3372383","title":"Adversarial Examples Detection With Bayesian Neural Network","display_name":"Adversarial Examples Detection With Bayesian Neural Network","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392908096","doi":"https://doi.org/10.1109/tetci.2024.3372383"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2024.3372383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2024.3372383","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100411246","display_name":"Yao Li","orcid":"https://orcid.org/0000-0002-7195-5774"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Li","raw_affiliation_strings":["Statistics and Operations Research Department, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"raw_orcid":"https://orcid.org/0000-0002-7195-5774","affiliations":[{"raw_affiliation_string":"Statistics and Operations Research Department, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063102978","display_name":"Tongyi Tang","orcid":"https://orcid.org/0009-0007-1223-5922"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tongyi Tang","raw_affiliation_strings":["Statistics Department, University of California, Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-1223-5922","affiliations":[{"raw_affiliation_string":"Statistics Department, University of California, Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010841999","display_name":"Cho\u2010Jui Hsieh","orcid":"https://orcid.org/0000-0002-3520-9627"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3520-9627","affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049635087","display_name":"Thomas C. M. Lee","orcid":"https://orcid.org/0000-0001-7067-405X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas C. M. Lee","raw_affiliation_strings":["Statistics Department, University of California, Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7067-405X","affiliations":[{"raw_affiliation_string":"Statistics Department, University of California, Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":1.1618,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.80643257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"8","issue":"5","first_page":"3654","last_page":"3664"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9549999833106995,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6064833998680115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.584845781326294},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5572060942649841},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5331003069877625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5291828513145447},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.43335938453674316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40535426139831543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3758062422275543}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6064833998680115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.584845781326294},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5572060942649841},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5331003069877625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5291828513145447},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.43335938453674316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40535426139831543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3758062422275543}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2024.3372383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2024.3372383","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2045930519","display_name":"DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges","funder_award_id":"2113605","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2102708625","display_name":"FRG: Collaborative Research: Mathematical and Statistical Analysis of Compressible Data on Compressive Networks","funder_award_id":"2152289","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3069504102","display_name":"HDR TRIPODS: UC Davis TETRAPODS Institute of Data Science","funder_award_id":"1934568","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3812866752","display_name":"CAREER: Robustness Verification and Certified Defense for Machine Learning Models","funder_award_id":"2048280","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4133067109","display_name":"RTG: Networks: Foundations in Probability, Optimization, and Data Sciences","funder_award_id":"2134107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5233990263","display_name":"RI: Small: Learning to Optimize: Designing and Improving Optimizers by Machine Learning Algorithms","funder_award_id":"2008173","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G645903391","display_name":"Collaborative Research: Emerging Variants of Generalized Fiducial Inference","funder_award_id":"2210388","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W2243397390","https://openalex.org/W2543927648","https://openalex.org/W2605631833","https://openalex.org/W2618043096","https://openalex.org/W2746600820","https://openalex.org/W2778624544","https://openalex.org/W2793165286","https://openalex.org/W2891828758","https://openalex.org/W2903810922","https://openalex.org/W2909079677","https://openalex.org/W2942465044","https://openalex.org/W2962710014","https://openalex.org/W2963178695","https://openalex.org/W2963542245","https://openalex.org/W2963564844","https://openalex.org/W2963612069","https://openalex.org/W2963857521","https://openalex.org/W2970257215","https://openalex.org/W2982054702","https://openalex.org/W2985386758","https://openalex.org/W2998279441","https://openalex.org/W2998600476","https://openalex.org/W3009153202","https://openalex.org/W3013068546","https://openalex.org/W3015625436","https://openalex.org/W3088909400","https://openalex.org/W3095533603","https://openalex.org/W3101628297","https://openalex.org/W3106412272","https://openalex.org/W3118608800","https://openalex.org/W3120349939","https://openalex.org/W3122061855","https://openalex.org/W3131738409","https://openalex.org/W3134853163","https://openalex.org/W3153236199","https://openalex.org/W3177409747","https://openalex.org/W3177428785","https://openalex.org/W3196874059","https://openalex.org/W3204341526","https://openalex.org/W3208354430","https://openalex.org/W3215624426","https://openalex.org/W4225262970","https://openalex.org/W4225790798","https://openalex.org/W4293584023","https://openalex.org/W4295803779","https://openalex.org/W4296340047","https://openalex.org/W4312641744","https://openalex.org/W4318823998","https://openalex.org/W4379538695","https://openalex.org/W4385573785"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,42,61],"propose":[4,43,76],"a":[5,35,44,104,115],"new":[6],"framework":[7],"to":[8,29,52,77,86,97],"detect":[9,98],"adversarial":[10,47,57,73,99,144],"examples":[11],"motivated":[12],"by":[13],"the":[14,21,31,54,63,79,82,94,110,138,140],"observations":[15],"that":[16,109,137],"random":[17,120],"components":[18,121],"can":[19],"improve":[20,53],"smoothness":[22],"of":[23,34,56,66,81,103],"predictors":[24],"and":[25,72,75,92],"make":[26],"it":[27],"easier":[28],"simulate":[30,87],"output":[32,69,90,111],"distribution":[33,91,95],"deep":[36,116],"neural":[37,84,106,117],"network.":[38],"With":[39],"these":[40],"observations,":[41],"novel":[45],"Bayesian":[46,83,105],"example":[48,58,145],"detector,":[49],"short":[50],"forBATer,":[51],"performance":[55],"detection.":[59,146],"Specifically,":[60],"study":[62],"distributional":[64],"difference":[65],"hidden":[67,88],"layer":[68,89],"between":[70],"natural":[71],"examples,":[74],"use":[78],"randomness":[80],"network":[85,107,118],"leverage":[93],"dispersion":[96],"examples.":[100],"The":[101],"advantage":[102],"is":[108,112],"stochastic":[113],"while":[114],"without":[119],"does":[122],"not":[123],"have":[124],"such":[125],"characteristics.":[126],"Empirical":[127],"results":[128],"on":[129],"several":[130],"benchmark":[131],"datasets":[132],"against":[133],"popular":[134],"attacks":[135],"show":[136],"proposedBATeroutperforms":[139],"state-of-the-art":[141],"detectors":[142],"in":[143]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}