{"id":"https://openalex.org/W3209929369","doi":"https://doi.org/10.1109/wacv51458.2022.00287","title":"Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis","display_name":"Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3209929369","doi":"https://doi.org/10.1109/wacv51458.2022.00287","mag":"3209929369"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv51458.2022.00287","pdf_url":null,"source":{"id":"https://openalex.org/S4363607979","display_name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.06405","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026964758","display_name":"Nathan Drenkow","orcid":null},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathan Drenkow","raw_affiliation_strings":["The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085546275","display_name":"Neil Fendley","orcid":null},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neil Fendley","raw_affiliation_strings":["The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078445265","display_name":"Philippe Burlina","orcid":"https://orcid.org/0000-0002-6353-0880"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philippe Burlina","raw_affiliation_strings":["The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"],"affiliations":[{"raw_affiliation_string":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723","institution_ids":["https://openalex.org/I2802946424"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.642,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.658299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":80,"max":83},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998,"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":0.9998,"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9788,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9732,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.46591997}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7860078},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7774127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7751105},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.6386132},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6199637},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5840981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5067778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.50172997},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.47653303},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.46591997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4228507},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33890375},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14134899},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv51458.2022.00287","pdf_url":null,"source":{"id":"https://openalex.org/S4363607979","display_name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2012.06405","pdf_url":"https://arxiv.org/pdf/2012.06405","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2012.06405","pdf_url":"https://arxiv.org/pdf/2012.06405","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":74,"referenced_works":["https://openalex.org/W108019484","https://openalex.org/W1531259569","https://openalex.org/W1945616565","https://openalex.org/W2089497633","https://openalex.org/W2132870739","https://openalex.org/W2180612164","https://openalex.org/W2194775991","https://openalex.org/W2561975083","https://openalex.org/W2590523583","https://openalex.org/W2594867206","https://openalex.org/W2607219512","https://openalex.org/W2616028256","https://openalex.org/W2735607295","https://openalex.org/W2771622532","https://openalex.org/W2786163515","https://openalex.org/W2788848944","https://openalex.org/W2793165286","https://openalex.org/W2793633339","https://openalex.org/W2794609696","https://openalex.org/W2804337238","https://openalex.org/W2809182766","https://openalex.org/W2867167548","https://openalex.org/W2907511876","https://openalex.org/W2913624634","https://openalex.org/W2914422142","https://openalex.org/W2914897181","https://openalex.org/W2943801311","https://openalex.org/W2949003498","https://openalex.org/W2950106672","https://openalex.org/W2950468330","https://openalex.org/W2963158386","https://openalex.org/W2963196925","https://openalex.org/W2963207607","https://openalex.org/W2963262327","https://openalex.org/W2963440492","https://openalex.org/W2963446712","https://openalex.org/W2963496101","https://openalex.org/W2963564844","https://openalex.org/W2963592643","https://openalex.org/W2963612069","https://openalex.org/W2963626025","https://openalex.org/W2963857521","https://openalex.org/W2963913218","https://openalex.org/W2963952467","https://openalex.org/W2963998105","https://openalex.org/W2964253222","https://openalex.org/W2970115835","https://openalex.org/W2972262177","https://openalex.org/W2979473749","https://openalex.org/W2981435674","https://openalex.org/W2991067485","https://openalex.org/W2997212544","https://openalex.org/W2998709064","https://openalex.org/W3005384972","https://openalex.org/W3034230713","https://openalex.org/W3037906261","https://openalex.org/W3044129898","https://openalex.org/W3090855408","https://openalex.org/W3099444373","https://openalex.org/W3105009650","https://openalex.org/W3113206159","https://openalex.org/W3136937816","https://openalex.org/W3173026327","https://openalex.org/W3183754277","https://openalex.org/W3186685300","https://openalex.org/W4287072220","https://openalex.org/W4287555433","https://openalex.org/W4288359148","https://openalex.org/W4293400368","https://openalex.org/W4293584023","https://openalex.org/W4293846201","https://openalex.org/W4297573953","https://openalex.org/W4299147078","https://openalex.org/W4394663350"],"related_works":["https://openalex.org/W4287164812","https://openalex.org/W3213150849","https://openalex.org/W3172436493","https://openalex.org/W3100286349","https://openalex.org/W3048732067","https://openalex.org/W2957492749","https://openalex.org/W2896134808","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W1887135636"],"abstract_inverted_index":{"Whilst":[0],"adversarial":[1,122,143],"attack":[2,169],"detection":[3,34,48,62,157],"has":[4],"received":[5],"considerable":[6],"attention,":[7],"it":[8],"remains":[9],"a":[10,53,97,107,125],"fundamentally":[11],"challenging":[12],"problem":[13],"from":[14,142],"two":[15],"perspectives.":[16],"First,":[17],"while":[18,163],"threat":[19],"models":[20],"can":[21],"be":[22,36],"well-defined,":[23],"attacker":[24],"strategies":[25,158],"may":[26],"still":[27],"vary":[28],"widely":[29],"within":[30],"those":[31],"constraints.":[32],"Therefore,":[33],"should":[35],"considered":[37],"as":[38],"an":[39],"open-set":[40],"problem,":[41],"standing":[42],"in":[43],"contrast":[44],"to":[45,115,139,167,190],"most":[46],"current":[47],"approaches.":[49],"These":[50],"methods":[51],"take":[52],"closed-set":[54],"view":[55],"and":[56,77,86,121],"train":[57],"binary":[58],"detectors,":[59],"thus":[60],"biasing":[61],"toward":[63],"attacks":[64],"seen":[65],"during":[66],"detector":[67],"training.":[68],"Second,":[69],"limited":[70],"information":[71],"is":[72,137],"available":[73],"at":[74],"test":[75],"time":[76],"typically":[78],"confounded":[79],"by":[80],"nuisance":[81],"factors":[82],"including":[83],"the":[84,90,117,168],"label":[85],"underlying":[87],"content":[88],"of":[89,112,119,128,134,184],"image.":[91],"We":[92,105],"address":[93],"these":[94],"challenges":[95],"via":[96],"novel":[98],"strategy":[99,170],"based":[100],"on":[101],"random":[102,113],"sub-space":[103],"analysis.":[104],"present":[106],"technique":[108,150],"that":[109,148],"utilizes":[110],"properties":[111],"projections":[114],"characterize":[116],"behavior":[118],"clean":[120,141,185],"examples":[123],"across":[124],"diverse":[126],"set":[127],"subspaces.":[129],"The":[130],"self-consistency":[131],"(or":[132],"inconsistency)":[133],"model":[135],"activations":[136],"leveraged":[138],"discern":[140],"examples.":[144],"Performance":[145],"evaluations":[146],"demonstrate":[147],"our":[149],"(AUC":[151,159],"\u2208":[152,160],"[0.92,":[153],"0.98])":[154],"outperforms":[155],"competing":[156,188],"[0.30,":[161],"0.79]),":[162],"remaining":[164],"truly":[165],"agnostic":[166],"(for":[171],"both":[172],"targeted/untargeted":[173],"attacks).":[174],"It":[175],"also":[176],"requires":[177],"significantly":[178],"less":[179],"calibration":[180],"data":[181],"(composed":[182],"only":[183],"examples)":[186],"than":[187],"approaches":[189],"achieve":[191],"this":[192],"performance.":[193]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3209929369","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2024-12-10T10:16:40.612019","created_date":"2021-11-08"}