{"id":"https://openalex.org/W2963034614","doi":"https://doi.org/10.1609/aaai.v33i01.3301541","title":"Resisting Adversarial Attacks Using Gaussian Mixture Variational Autoencoders","display_name":"Resisting Adversarial Attacks Using Gaussian Mixture Variational Autoencoders","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2963034614","doi":"https://doi.org/10.1609/aaai.v33i01.3301541","mag":"2963034614"},"language":"en","primary_location":{"id":"pmh:oai:ojs.aaai.org:article/3828","doi":"https://doi.org/10.1609/aaai.v33i01.3301541","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/3828","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/3828/3706","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/3828/3706","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001735719","display_name":"Partha Sarathi Ghosh","orcid":"https://orcid.org/0000-0003-4808-3370","observed_orcids":["https://orcid.org/0000-0003-4808-3370"]},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ghosh, Partha","raw_affiliation_strings":["Max Planck Institute for Intelligent Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Intelligent Systems","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032057825","display_name":"Arpan Losalka","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Losalka, Arpan","raw_affiliation_strings":["IBM Research AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065396778","display_name":"Michael J. Black","orcid":"https://orcid.org/0000-0001-6077-4540","observed_orcids":["https://orcid.org/0000-0001-6077-4540"]},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Black, Michael J.","raw_affiliation_strings":["Max Planck Institute for Intelligent Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Intelligent Systems","institution_ids":["https://openalex.org/I4210135521"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4011,"has_fulltext":true,"cited_by_count":71,"citation_normalized_percentile":{"value":0.95408352,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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/T11424","display_name":"Security and Verification in Computing","score":0.0,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/adversarial-attacks","display_name":"adversarial attacks","score":0.8960000276565552},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.7739999890327454},{"id":"https://openalex.org/keywords/gaussian-mixture-variational-autoencoder","display_name":"Gaussian mixture variational autoencoder","score":0.8460000157356262},{"id":"https://openalex.org/keywords/selective-prediction","display_name":"selective prediction","score":0.7950000166893005},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.9380000233650208},{"id":"https://openalex.org/keywords/latent-variable-modeling","display_name":"latent variable models","score":0.5680000185966492}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8916415572166443},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7828559875488281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6711281538009644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6562920212745667},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6155941486358643},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5855081081390381},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5791819095611572},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5585649609565735},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5198319554328918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.505486249923706},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43887677788734436},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4285608232021332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4011390209197998},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:ojs.aaai.org:article/3828","doi":"https://doi.org/10.1609/aaai.v33i01.3301541","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/3828","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/3828/3706","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ojs.aaai.org:article/3828","doi":"https://doi.org/10.1609/aaai.v33i01.3301541","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/3828","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/3828/3706","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963034614.pdf","grobid_xml":"https://content.openalex.org/works/W2963034614.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W191001584","https://openalex.org/W1522301498","https://openalex.org/W1673923490","https://openalex.org/W1677182931","https://openalex.org/W1883420340","https://openalex.org/W1932198206","https://openalex.org/W1945616565","https://openalex.org/W1959608418","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2174868984","https://openalex.org/W2180612164","https://openalex.org/W2207027850","https://openalex.org/W2243397390","https://openalex.org/W2335728318","https://openalex.org/W2408141691","https://openalex.org/W2516574342","https://openalex.org/W2517229335","https://openalex.org/W2528914598","https://openalex.org/W2603766943","https://openalex.org/W2612246789","https://openalex.org/W2640329709","https://openalex.org/W2746600820","https://openalex.org/W2765233338","https://openalex.org/W2766108848","https://openalex.org/W2768899812","https://openalex.org/W2774644650","https://openalex.org/W2787496614","https://openalex.org/W2787708942","https://openalex.org/W2797142180","https://openalex.org/W2798302089","https://openalex.org/W2804545960","https://openalex.org/W2819579046","https://openalex.org/W2949311987","https://openalex.org/W2963446712","https://openalex.org/W2963981733"],"related_works":["https://openalex.org/W4205145874","https://openalex.org/W4389041603","https://openalex.org/W3013135147","https://openalex.org/W3095379301","https://openalex.org/W3193828372","https://openalex.org/W4221153117","https://openalex.org/W2969549713","https://openalex.org/W3097058189","https://openalex.org/W2905483182","https://openalex.org/W3009556147"],"abstract_inverted_index":{"Susceptibility":[0],"of":[1,32,81,131,135,152],"deep":[2,36],"neural":[3,37],"networks":[4,38],"to":[5,17,53,100,120,128],"adversarial":[6,118,132,165,176],"attacks":[7,22],"poses":[8],"a":[9,73,82,86,101,122,150,154,158],"major":[10],"theoretical":[11],"and":[12,43,141],"practical":[13],"challenge.":[14],"All":[15],"efforts":[16],"harden":[18],"classifiers":[19],"against":[20,34,69],"such":[21,94],"have":[23,46],"seen":[24],"limited":[25],"success":[26],"till":[27],"now.":[28],"Two":[29],"distinct":[30],"categories":[31],"samples":[33,133,177],"which":[35,161],"are":[39],"vulnerable,":[40],"\u201cadversarial":[41],"samples\u201d":[42],"\u201cfooling":[44],"samples\u201d,":[45],"been":[47],"tackled":[48],"separately":[49],"so":[50],"far":[51],"due":[52],"the":[54,79,91,117,129,174],"difficulty":[55],"posed":[56],"when":[57],"considered":[58],"together.":[59],"In":[60],"this":[61,113],"work,":[62],"we":[63],"show":[64,105,169],"how":[65,106,170],"one":[66,171],"can":[67,109,162,172],"defend":[68],"them":[70],"both":[71],"under":[72],"unified":[74],"framework.":[75],"Our":[76],"model":[77],"has":[78],"form":[80],"variational":[83],"autoencoder":[84],"with":[85],"Gaussian":[87],"mixture":[88,97],"prior":[89],"on":[90,143],"latent":[92],"variable,":[93],"that":[95],"each":[96],"component":[98],"corresponds":[99],"single":[102],"class.":[103],"We":[104,167],"selective":[107,155],"classification":[108],"be":[110],"performed":[111],"using":[112],"model,":[114],"thereby":[115],"causing":[116],"objective":[119],"entail":[121],"conflict.":[123],"The":[124],"proposed":[125],"method":[126],"leads":[127],"rejection":[130],"instead":[134],"misclassification,":[136],"while":[137],"maintaining":[138],"high":[139],"precision":[140],"recall":[142],"test":[144],"data.":[145],"It":[146],"also":[147],"inherently":[148],"provides":[149],"way":[151],"learning":[153],"classifier":[156],"in":[157],"semi-supervised":[159],"scenario,":[160],"similarly":[163],"resist":[164],"attacks.":[166],"further":[168],"reclassify":[173],"detected":[175],"by":[178],"iterative":[179],"optimization.1":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-07-30T00:00:00"}