{"id":"https://openalex.org/W4224923640","doi":"https://doi.org/10.1109/icassp43922.2022.9747469","title":"Real-World Adversarial Examples Via Makeup","display_name":"Real-World Adversarial Examples Via Makeup","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224923640","doi":"https://doi.org/10.1109/icassp43922.2022.9747469"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747469","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5102796146","display_name":"Chang-Sheng Lin","orcid":"https://orcid.org/0000-0001-7312-1979"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chang-Sheng Lin","raw_affiliation_strings":["Nation Chung Hsing University"],"affiliations":[{"raw_affiliation_string":"Nation Chung Hsing University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035387029","display_name":"Chia-Yi Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Yi Hsu","raw_affiliation_strings":["National Yang Ming Chiao Tung University"],"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050344371","display_name":"Pin\u2010Yu Chen","orcid":"https://orcid.org/0000-0003-1039-8369"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pin-Yu Chen","raw_affiliation_strings":["IBM Thomas J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085568918","display_name":"Chia-Mu Yu","orcid":"https://orcid.org/0000-0002-1677-2131"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chia-Mu Yu","raw_affiliation_strings":["Nation Chung Hsing University"],"affiliations":[{"raw_affiliation_string":"Nation Chung Hsing University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102796146"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3512,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.82449903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2854","last_page":"2858"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9961000084877014,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9879999756813049,"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":0.9481027126312256},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7774355411529541},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7183794975280762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.660741925239563},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6447840332984924},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5647974014282227},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5542195439338684},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5361518859863281},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4741111397743225},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45123156905174255},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.44874307513237},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4374350607395172},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4251795709133148},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3210670053958893}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9481027126312256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7774355411529541},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7183794975280762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.660741925239563},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6447840332984924},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5647974014282227},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5542195439338684},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5361518859863281},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4741111397743225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45123156905174255},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.44874307513237},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4374350607395172},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4251795709133148},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3210670053958893},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747469","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7799999713897705,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2535873859","https://openalex.org/W2616625561","https://openalex.org/W2774644650","https://openalex.org/W2962793481","https://openalex.org/W2963857521","https://openalex.org/W2969664989","https://openalex.org/W2969985801","https://openalex.org/W2970255644","https://openalex.org/W2972986629","https://openalex.org/W2981011605","https://openalex.org/W3015646845","https://openalex.org/W3034823537","https://openalex.org/W3083540668","https://openalex.org/W3097573595","https://openalex.org/W3101267861","https://openalex.org/W3110144845","https://openalex.org/W3119391778","https://openalex.org/W3189402954","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6739868092","https://openalex.org/W6766699755"],"related_works":["https://openalex.org/W3156763702","https://openalex.org/W3156291593","https://openalex.org/W3198184493","https://openalex.org/W4380925710","https://openalex.org/W4300176179","https://openalex.org/W2781758978","https://openalex.org/W2963165363","https://openalex.org/W2787496614","https://openalex.org/W2964197269","https://openalex.org/W4254236733"],"abstract_inverted_index":{"Deep":[0],"neural":[1,36],"networks":[2],"have":[3,7,25],"developed":[4],"rapidly":[5],"and":[6,18,30,96,108,136],"achieved":[8],"out-standing":[9],"performance":[10],"in":[11,42,130],"several":[12],"tasks,":[13],"such":[14,133],"as":[15,134],"image":[16],"classification":[17],"natural":[19],"language":[20],"processing.":[21],"However,":[22],"recent":[23],"studies":[24],"indicated":[26],"that":[27,45,122,142],"both":[28],"digital":[29],"physical":[31,50,57,152],"adversarial":[32,51,58,106,155],"examples":[33],"can":[34,125,150],"fool":[35],"networks.":[37],"Face-recognition":[38],"systems":[39],"are":[40,162],"used":[41,103,145],"various":[43],"applications":[44],"involve":[46],"security":[47],"threats":[48],"from":[49,158],"examples.":[52],"Herein,":[53],"we":[54,89],"propose":[55],"a":[56,75,97],"attack":[59,87,124],"with":[60],"the":[61,71,81,91,109,112,143,148,154,159,165],"use":[62],"of":[63,68,83,111],"full-face":[64],"makeup.":[65],"The":[66,100],"presence":[67],"makeup":[69,131],"on":[70],"human":[72],"face":[73],"is":[74,102,115],"reasonable":[76],"possibility,":[77],"which":[78],"possibly":[79],"increases":[80],"imperceptibility":[82],"attacks.":[84],"In":[85],"our":[86,123],"framework,":[88],"combine":[90],"cycle-adversarial":[92],"generative":[93],"network":[94],"(cycle-GAN)":[95],"victimized":[98,113],"classifier.":[99],"Cycle-GAN":[101],"to":[104,146],"generate":[105],"makeup,":[107],"architecture":[110],"classifier":[114],"VGG":[116],"16.":[117],"Our":[118],"experimental":[119],"results":[120],"show":[121],"effectively":[126],"overcome":[127],"manual":[128],"errors":[129],"application,":[132],"color":[135],"position-related":[137],"errors.":[138],"We":[139],"also":[140],"demonstrate":[141],"approaches":[144],"train":[147],"models":[149],"influence":[151],"attacks;":[153],"perturbations":[156],"crafted":[157],"pre-trained":[160],"model":[161],"affected":[163],"by":[164],"corresponding":[166],"training":[167],"data.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}