{"id":"https://openalex.org/W2890500756","doi":"https://doi.org/10.13016/m29z90g37","title":"Machine Learning of Facial Attributes Using Explainable, Secure and Generative Adversarial Networks","display_name":"Machine Learning of Facial Attributes Using Explainable, Secure and Generative Adversarial Networks","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2890500756","doi":"https://doi.org/10.13016/m29z90g37","mag":"2890500756"},"language":"en","primary_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/21362","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M29Z90G37","pdf_url":"http://hdl.handle.net/1903/21362","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/1903/21362","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088895740","display_name":"Pouya Samangouei","orcid":"https://orcid.org/0000-0002-4443-5175"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Samangouei, Pouya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5088895740"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11448","display_name":"Face recognition and analysis","score":0.9907000064849854,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9907000064849854,"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-system","display_name":"Adversarial system","score":0.7835575342178345},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7194209694862366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5424396395683289},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5156294107437134},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5045267343521118},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39664316177368164},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3225887417793274}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7835575342178345},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7194209694862366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5424396395683289},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5156294107437134},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5045267343521118},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39664316177368164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3225887417793274}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:drum.lib.umd.edu:1903/21362","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M29Z90G37","pdf_url":"http://hdl.handle.net/1903/21362","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},{"id":"mag:2890500756","is_oa":false,"landing_page_url":"https://drum.lib.umd.edu/handle/1903/21362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null},{"id":"doi:10.13016/m29z90g37","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m29z90g37","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Collection"}],"best_oa_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/21362","is_oa":true,"landing_page_url":"https://doi.org/10.13016/M29Z90G37","pdf_url":"http://hdl.handle.net/1903/21362","source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2890500756.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3015573934","https://openalex.org/W3157232645","https://openalex.org/W3100490876","https://openalex.org/W3042239647","https://openalex.org/W2963633072","https://openalex.org/W3178813832","https://openalex.org/W3130140376","https://openalex.org/W2765655769","https://openalex.org/W3036924646","https://openalex.org/W2802860659","https://openalex.org/W3026821452","https://openalex.org/W3126286373","https://openalex.org/W2948082843","https://openalex.org/W3214319091","https://openalex.org/W3049493966","https://openalex.org/W3170848816","https://openalex.org/W2948605516","https://openalex.org/W3203790719","https://openalex.org/W2905027213","https://openalex.org/W2989953367"],"abstract_inverted_index":{"\"Attributes\"":[0],"are":[1,23,57,423],"referred":[2],"to":[3,8,43,59,71,78,96,112,171,199,223,263,272,280,326,342,368,378,435,448,456],"abstractions":[4],"that":[5,13,143,352,386,445,464],"humans":[6,32,317],"use":[7,333,454],"group":[9],"entities":[10],"and":[11,33,318,375,453,458],"phenomena":[12],"have":[14,38],"a":[15,141,211,220,252,256,303,323,393,428],"common":[16],"characteristic.":[17],"In":[18],"machine":[19],"learning":[20,162],"(ML),":[21],"attributes":[22,129,270],"fundamental":[24],"because":[25],"they":[26,422],"bridge":[27],"the":[28,53,81,99,114,118,126,132,178,201,217,230,267,281,290,313,328,344,365,369,372,387,404,441,450,465],"semantic":[29,100,133,314,419],"gap":[30,101,134,315,420],"between":[31,316],"ML":[34,46,61,137],"systems.":[35,138],"Thus,":[36],"researchers":[37],"been":[39],"using":[40,168,205,299],"this":[41,173,240,437],"concept":[42],"transform":[44],"complicated":[45,136],"systems":[47,62],"into":[48,153],"interactive":[49,155],"ones.":[50],"However,":[51],"training":[52,335,442],"attribute":[54,73,103,202,235,250,275,319,330,348,388,405,472],"detectors":[55,104,203],"which":[56,84,295,362],"central":[58],"attribute-based":[60,156],"can":[63,85,360],"still":[64],"be":[65,69],"challenging.":[66],"It":[67],"might":[68],"infeasible":[70],"gather":[72],"labels":[74],"for":[75,130,188,215,469],"rare":[76],"combinations":[77],"cover":[79],"all":[80],"corner":[82],"cases,":[83],"result":[86],"in":[87,98,117,135,163,255,312,336,382,392],"weak":[88],"detectors.":[89,331,473],"Also,":[90],"it":[91,107,455],"is":[92,108,357,446,467],"not":[93,109,264,297],"clear":[94],"how":[95,111,177,377],"fill":[97],"with":[102,165,193,302,338,396],"themselves.":[105],"Finally,":[106,399],"obvious":[110],"interpret":[113],"detectors'":[115],"outputs":[116],"presence":[119,268],"of":[120,128,147,185,219,242,269,293,346,364,371],"adversarial":[121,334,412,433],"noise.":[122],"First,":[123],"we":[124,197,284,400],"investigate":[125,310],"effectiveness":[127],"bridging":[131],"We":[139,158,175,209,238,308,332,350,426,439,462],"turn":[140],"system":[142,180],"does":[144],"continuous":[145],"authentication":[146,189],"human":[148,397],"faces":[149],"on":[150,190,233,402,431],"mobile":[151,191],"phones":[152,192],"an":[154,274,339,417],"one.":[157],"employ":[159],"deep":[160,186],"multi-task":[161],"conjunction":[164,337],"multi-view":[166],"classification":[167],"facial":[169,471],"parts":[170],"tackle":[172],"problem.":[174],"show":[176,285,351,463],"proposed":[179,355],"decomposition":[181],"enables":[182],"efficient":[183],"deployment":[184],"networks":[187,434],"limited":[194],"resources.":[195],"Next,":[196],"seek":[198],"improve":[200],"by":[204,247,321,411],"conditional":[206],"image":[207,222,279,287,366,380],"synthesis.":[208],"take":[210,239],"generative":[212,432],"modeling":[213,248],"approach":[214],"manipulating":[216],"semantics":[218],"given":[221],"provide":[224],"novel":[225],"examples.":[226],"Previous":[227],"works":[228],"condition":[229],"generation":[231,288],"process":[232],"binary":[234,347],"existence":[236],"values.":[237],"type":[241],"approaches":[243],"one":[244,278,359],"step":[245],"further":[246],"each":[249],"as":[251],"distributed":[253],"representation":[254],"vector":[257],"space.":[258],"These":[259,414],"representations":[260],"allow":[261],"us":[262],"only":[265],"toggle":[266],"but":[271],"transfer":[273],"style":[276],"from":[277,289,407],"other.":[282],"Furthermore,":[283],"diverse":[286],"same":[291],"set":[292],"conditions,":[294],"was":[296],"possible":[298],"existing":[300],"methods":[301],"single":[304],"dimension":[305],"per":[306],"attribute.":[307],"then":[309],"filling":[311],"classifiers":[320],"proposing":[322],"new":[324],"way":[325,395],"explain":[327],"pre-trained":[329],"encoder-decoder":[340],"model":[341,356],"learn":[343,440],"behavior":[345],"classifiers.":[349],"after":[353],"our":[354],"trained,":[358],"see":[361],"areas":[363,384],"contribute":[367],"presence/absence":[370],"target":[373],"attribute,":[374],"also":[376],"change":[379],"pixels":[381],"those":[383],"so":[385],"classifier":[389,452],"decision":[390],"changes":[391],"consistent":[394],"perception.":[398],"focus":[401],"protecting":[403],"models":[406],"un-interpretable":[408],"behaviors":[409,415],"provoked":[410],"perturbations.":[413],"create":[416],"inexplainable":[418],"since":[421],"visually":[424],"unnoticeable.":[425],"propose":[427],"method":[429,466],"based":[430],"alleviate":[436],"issue.":[438],"data":[443],"distribution":[444],"used":[447],"train":[449],"core":[451],"detect":[457],"denoise":[459],"test":[460],"samples.":[461],"effective":[468],"defending":[470]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}