{"id":"https://openalex.org/W3034856597","doi":"https://doi.org/10.24963/ijcai.2020/122","title":"Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error","display_name":"Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034856597","doi":"https://doi.org/10.24963/ijcai.2020/122","mag":"3034856597"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/122","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/122","pdf_url":"https://www.ijcai.org/proceedings/2020/0122.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0122.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063798437","display_name":"Celia Cintas","orcid":"https://orcid.org/0000-0002-8064-9189"},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Celia Cintas","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029048857","display_name":"Skyler Speakman","orcid":"https://orcid.org/0000-0003-0337-2312"},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Skyler Speakman","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075828823","display_name":"Victor Akinwande","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Victor Akinwande","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040949314","display_name":"William Ogallo","orcid":"https://orcid.org/0000-0001-8580-1656"},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"William Ogallo","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040195857","display_name":"Komminist Weldemariam","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Komminist Weldemariam","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011753153","display_name":"Srihari Sridharan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162937","display_name":"IBM Research - Africa","ror":"https://ror.org/05c0m9m16","country_code":"ZA","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210162937"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Srihari Sridharan","raw_affiliation_strings":["IBM Research Africa","IBM Research Africa;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Africa","institution_ids":["https://openalex.org/I4210162937"]},{"raw_affiliation_string":"IBM Research Africa;","institution_ids":["https://openalex.org/I4210162937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026872292","display_name":"Edward McFowland","orcid":"https://orcid.org/0000-0001-5249-7117"},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward McFowland","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"876","last_page":"882"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9927999973297119,"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":0.991100013256073,"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/autoencoder","display_name":"Autoencoder","score":0.765695333480835},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7546943426132202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.661687433719635},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5087190270423889},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.4989628791809082},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4902527630329132},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4891863167285919},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47594785690307617},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4363349378108978},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4064517021179199},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18810367584228516}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.765695333480835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7546943426132202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.661687433719635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5087190270423889},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.4989628791809082},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4902527630329132},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4891863167285919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47594785690307617},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4363349378108978},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4064517021179199},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18810367584228516}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/122","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/122","pdf_url":"https://www.ijcai.org/proceedings/2020/0122.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/122","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/122","pdf_url":"https://www.ijcai.org/proceedings/2020/0122.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034856597.pdf","grobid_xml":"https://content.openalex.org/works/W3034856597.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W1974169660","https://openalex.org/W1976526581","https://openalex.org/W2038943544","https://openalex.org/W2112796928","https://openalex.org/W2123659014","https://openalex.org/W2127979711","https://openalex.org/W2132870739","https://openalex.org/W2146022760","https://openalex.org/W2243397390","https://openalex.org/W2398119937","https://openalex.org/W2460937040","https://openalex.org/W2516373086","https://openalex.org/W2618043096","https://openalex.org/W2743138268","https://openalex.org/W2750384547","https://openalex.org/W2901122692","https://openalex.org/W2949311987","https://openalex.org/W2963207607","https://openalex.org/W2964153729","https://openalex.org/W2964197269","https://openalex.org/W2983044655","https://openalex.org/W3015625436","https://openalex.org/W3032026348","https://openalex.org/W3118608800","https://openalex.org/W3121234099","https://openalex.org/W4293846201","https://openalex.org/W4300511536"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"Reliably":[0],"detecting":[1,50],"attacks":[2,52],"in":[3,31,53,71],"a":[4,28,62,194],"given":[5],"set":[6],"of":[7,10,15,18,56,65,131,152,197],"inputs":[8,26],"is":[9],"high":[11],"practical":[12],"relevance":[13],"because":[14],"the":[16,84,89,119,132,150,155],"vulnerability":[17],"neural":[19],"networks":[20,59,77],"to":[21,124,141,160,168,172],"adversarial":[22,51,190],"examples.":[23],"These":[24],"altered":[25],"create":[27],"security":[29],"risk":[30],"applications":[32],"with":[33],"real-world":[34],"consequences,":[35],"such":[36],"as":[37],"self-driving":[38],"cars,":[39],"robotics":[40],"and":[41,174,193],"financial":[42],"services.":[43],"We":[44],"propose":[45],"an":[46,142],"unsupervised":[47],"method":[48,147],"for":[49],"inner":[54],"layers":[55],"autoencoder":[57],"(AE)":[58],"by":[60,82,88],"maximizing":[61],"non-parametric":[63],"measure":[64],"anomalous":[66,80,120,143],"node":[67],"activations.":[68],"Previous":[69],"work":[70,167],"this":[72],"space":[73],"has":[74],"shown":[75],"AE":[76,156],"can":[78],"detect":[79],"images":[81],"thresholding":[83],"reconstruction":[85],"error":[86],"produced":[87],"final":[90],"layer.":[91],"Furthermore,":[92],"other":[93],"detection":[94,122,126,171,182,186],"methods":[95,117,187],"rely":[96],"on":[97],"data":[98,136],"augmentation":[99,137],"or":[100,135],"specialized":[101],"training":[102,109],"techniques":[103],"which":[104],"must":[105],"be":[106],"asserted":[107],"before":[108],"time.":[110],"In":[111,139],"contrast,":[112],"we":[113],"use":[114],"subset":[115,151],"scanning":[116,177],"from":[118,170],"pattern":[121],"domain":[123],"enhance":[125],"power":[127,183],"without":[128],"labeled":[129],"examples":[130],"noise,":[133],"retraining":[134],"methods.":[138],"addition":[140],"\u201cscore\u201d":[144],"our":[145],"proposed":[146],"also":[148],"returns":[149],"nodes":[153],"within":[154],"network":[157],"that":[158,161],"contributed":[159],"score.":[162],"This":[163],"will":[164],"allow":[165],"future":[166],"pivot":[169],"visualisation":[173],"explainability.":[175],"Our":[176],"approach":[178],"shows":[179],"consistently":[180],"higher":[181],"than":[184],"existing":[185],"across":[188],"several":[189],"noise":[191],"models":[192],"wide":[195],"range":[196],"perturbation":[198],"strengths.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}