{"id":"https://openalex.org/W2762664271","doi":"https://doi.org/10.1109/btas.2017.8272695","title":"LOTS about attacking deep features","display_name":"LOTS about attacking deep features","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2762664271","doi":"https://doi.org/10.1109/btas.2017.8272695","mag":"2762664271"},"language":"en","primary_location":{"id":"doi:10.1109/btas.2017.8272695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2017.8272695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-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/A5050278139","display_name":"Andras Rozsa","orcid":null},"institutions":[{"id":"https://openalex.org/I888729015","display_name":"University of Colorado Colorado Springs","ror":"https://ror.org/054spjc55","country_code":"US","type":"education","lineage":["https://openalex.org/I888729015"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andras Rozsa","raw_affiliation_strings":["Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA"],"affiliations":[{"raw_affiliation_string":"Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA","institution_ids":["https://openalex.org/I888729015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064221367","display_name":"Manuel G\u00fcnther","orcid":"https://orcid.org/0000-0003-1489-7448"},"institutions":[{"id":"https://openalex.org/I888729015","display_name":"University of Colorado Colorado Springs","ror":"https://ror.org/054spjc55","country_code":"US","type":"education","lineage":["https://openalex.org/I888729015"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manuel Gunther","raw_affiliation_strings":["Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA"],"affiliations":[{"raw_affiliation_string":"Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA","institution_ids":["https://openalex.org/I888729015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112354589","display_name":"Terranee E. Boult","orcid":null},"institutions":[{"id":"https://openalex.org/I888729015","display_name":"University of Colorado Colorado Springs","ror":"https://ror.org/054spjc55","country_code":"US","type":"education","lineage":["https://openalex.org/I888729015"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Terranee E. Boult","raw_affiliation_strings":["Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA"],"affiliations":[{"raw_affiliation_string":"Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA","institution_ids":["https://openalex.org/I888729015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050278139"],"corresponding_institution_ids":["https://openalex.org/I888729015"],"apc_list":null,"apc_paid":null,"fwci":2.9253,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.93021219,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"168","last_page":"176"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9929999709129333,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9861000180244446,"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/computer-science","display_name":"Computer science","score":0.7801491022109985},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7385613322257996},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.7149076461791992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6704700589179993},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6539306640625},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6070894002914429},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.5424637794494629},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48486027121543884},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.47303861379623413},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.45829591155052185},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.43234166502952576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4122452139854431},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4107152223587036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33185648918151855}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7801491022109985},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7385613322257996},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.7149076461791992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704700589179993},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6539306640625},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6070894002914429},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.5424637794494629},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48486027121543884},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.47303861379623413},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.45829591155052185},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.43234166502952576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4122452139854431},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4107152223587036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33185648918151855},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/btas.2017.8272695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2017.8272695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320312530","display_name":"Office of the Director of National Intelligence","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1533303231","https://openalex.org/W1673923490","https://openalex.org/W1834627138","https://openalex.org/W1895390915","https://openalex.org/W1945616565","https://openalex.org/W1949778830","https://openalex.org/W1998808035","https://openalex.org/W1999533590","https://openalex.org/W2017155271","https://openalex.org/W2062118960","https://openalex.org/W2096733369","https://openalex.org/W2097117768","https://openalex.org/W2101544546","https://openalex.org/W2108767394","https://openalex.org/W2133665775","https://openalex.org/W2144172034","https://openalex.org/W2145287260","https://openalex.org/W2152433273","https://openalex.org/W2155541015","https://openalex.org/W2194775991","https://openalex.org/W2325939864","https://openalex.org/W2336258321","https://openalex.org/W2963098487","https://openalex.org/W2963207607","https://openalex.org/W2963389226","https://openalex.org/W2963955657","https://openalex.org/W2964153729","https://openalex.org/W3099206234","https://openalex.org/W3101036461","https://openalex.org/W4293478066","https://openalex.org/W4293529028","https://openalex.org/W4294375521","https://openalex.org/W6631923913","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6681239517","https://openalex.org/W6682778277","https://openalex.org/W6685943642","https://openalex.org/W6700903540","https://openalex.org/W6729756640"],"related_works":["https://openalex.org/W3193857078","https://openalex.org/W3208304128","https://openalex.org/W2952919291","https://openalex.org/W2733060750","https://openalex.org/W4379255972","https://openalex.org/W2773120646","https://openalex.org/W1973212107","https://openalex.org/W3186111093","https://openalex.org/W4317987726","https://openalex.org/W2110594266"],"abstract_inverted_index":{"Deep":[0],"neural":[1,47],"networks":[2,48],"provide":[3],"state-of-the-art":[4],"performance":[5],"on":[6],"various":[7],"tasks":[8],"and":[9,38,143,165,177],"are,":[10],"therefore,":[11],"widely":[12],"used":[13,32,128],"in":[14,23,33],"real":[15],"world":[16],"applications.":[17],"DNNs":[18],"are":[19,97,184],"becoming":[20],"frequently":[21],"utilized":[22],"biometrics":[24],"for":[25,36,72],"extracting":[26],"deep":[27,46,95,136,167,182],"features,":[28],"which":[29],"can":[30,56,125],"be":[31,126],"recognition":[34],"systems":[35,93,155,180],"enrolling":[37],"recognizing":[39],"new":[40],"individuals.":[41],"It":[42],"was":[43],"revealed":[44],"that":[45,124,133,156,171,179],"suffer":[49],"from":[50],"a":[51,115],"fundamental":[52],"problem,":[53],"namely,":[54],"they":[55,79],"unexpectedly":[57],"misclassify":[58],"examples":[59,132],"formed":[60],"by":[61],"slightly":[62],"perturbing":[63],"correctly":[64],"recognized":[65],"inputs.":[66],"Various":[67],"approaches":[68],"have":[69],"been":[70],"developed":[71],"generating":[73],"these":[74],"so-called":[75],"adversarial":[76,131,146],"examples,":[77],"but":[78],"aim":[80],"at":[81,101],"attacking":[82],"end-to-end":[83,108,150,190],"networks.":[84,109],"For":[85],"biometrics,":[86],"it":[87],"is":[88,174],"natural":[89],"to":[90,99,105,129,186],"ask":[91],"whether":[92],"using":[94],"features":[96,137,183],"immune":[98],"or,":[100],"least,":[102],"more":[103],"resilient":[104],"attacks":[106],"than":[107,188],"In":[110],"this":[111],"paper,":[112],"we":[113],"introduce":[114],"general":[116],"technique":[117],"called":[118],"the":[119,135,139,145,149,189],"layerwise":[120],"origin-target":[121],"synthesis":[122],"(LOTS)":[123],"efficiently":[127],"form":[130],"mimic":[134],"of":[138,148],"target.":[140],"We":[141,169],"analyze":[142],"compare":[144],"robustness":[147],"VGG":[151],"Face":[152],"network":[153],"with":[154],"use":[157],"Euclidean":[158],"or":[159],"cosine":[160],"distance":[161],"between":[162],"gallery":[163],"templates":[164],"extracted":[166],"features.":[168],"demonstrate":[170],"iterative":[172],"LOTS":[173],"very":[175],"effective":[176],"show":[178],"utilizing":[181],"easier":[185],"attack":[187],"network.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
