{"id":"https://openalex.org/W2941733693","doi":"https://doi.org/10.1109/btas.2018.8698548","title":"Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?","display_name":"Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2941733693","doi":"https://doi.org/10.1109/btas.2018.8698548","mag":"2941733693"},"language":"en","primary_location":{"id":"doi:10.1109/btas.2018.8698548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2018.8698548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","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/A5024647585","display_name":"Akshay Agarwal","orcid":"https://orcid.org/0000-0001-7362-4752"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Akshay Agarwal","raw_affiliation_strings":["IIIT-Delhi, India and IBM, Watson, TJ, USA"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi, India and IBM, Watson, TJ, USA","institution_ids":["https://openalex.org/I68891433","https://openalex.org/I119939252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011779957","display_name":"Richa Singh","orcid":"https://orcid.org/0000-0003-4060-4573"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Richa Singh","raw_affiliation_strings":["IIIT-Delhi, India and IBM, Watson, TJ, USA"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi, India and IBM, Watson, TJ, USA","institution_ids":["https://openalex.org/I68891433","https://openalex.org/I119939252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050521702","display_name":"Mayank Vatsa","orcid":"https://orcid.org/0000-0001-5952-2274"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mayank Vatsa","raw_affiliation_strings":["IIIT-Delhi, India and IBM, Watson, TJ, USA"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi, India and IBM, Watson, TJ, USA","institution_ids":["https://openalex.org/I68891433","https://openalex.org/I119939252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003836761","display_name":"Nalini Ratha","orcid":"https://orcid.org/0000-0001-7913-5722"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nalini Ratha","raw_affiliation_strings":["IIIT-Delhi, India and IBM, Watson, TJ, USA"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi, India and IBM, Watson, TJ, USA","institution_ids":["https://openalex.org/I68891433","https://openalex.org/I119939252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024647585"],"corresponding_institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":5.2123,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.96427861,"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":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9916999936103821,"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":0.9779000282287598,"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-system","display_name":"Adversarial system","score":0.9130486249923706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.749801516532898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6494817733764648},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5719743967056274},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5672393441200256},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5169586539268494},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5108304023742676},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5022034645080566},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.48174169659614563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44922417402267456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42201608419418335},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4180552661418915}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9130486249923706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.749801516532898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6494817733764648},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5719743967056274},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5672393441200256},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5169586539268494},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5108304023742676},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5022034645080566},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.48174169659614563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44922417402267456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42201608419418335},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4180552661418915},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/btas.2018.8698548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2018.8698548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1524144700","https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1945616565","https://openalex.org/W2071266191","https://openalex.org/W2097117768","https://openalex.org/W2138451337","https://openalex.org/W2155893237","https://openalex.org/W2180612164","https://openalex.org/W2194775991","https://openalex.org/W2217248474","https://openalex.org/W2243397390","https://openalex.org/W2460937040","https://openalex.org/W2481800796","https://openalex.org/W2513314332","https://openalex.org/W2543927648","https://openalex.org/W2561975083","https://openalex.org/W2590523583","https://openalex.org/W2594867206","https://openalex.org/W2605631833","https://openalex.org/W2606529538","https://openalex.org/W2611576673","https://openalex.org/W2618492571","https://openalex.org/W2624607299","https://openalex.org/W2768149277","https://openalex.org/W2788741149","https://openalex.org/W2912990735","https://openalex.org/W2942039787","https://openalex.org/W2950468330","https://openalex.org/W2963047332","https://openalex.org/W2963184668","https://openalex.org/W2963207607","https://openalex.org/W2963389226","https://openalex.org/W2963564844","https://openalex.org/W2963612069","https://openalex.org/W2963694625","https://openalex.org/W2963695663","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W2964153729","https://openalex.org/W3105009650","https://openalex.org/W4239510810","https://openalex.org/W4293584023","https://openalex.org/W4295803779","https://openalex.org/W4297573953","https://openalex.org/W4300511536","https://openalex.org/W4381325153","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6640425456","https://openalex.org/W6688549366","https://openalex.org/W6719080892","https://openalex.org/W6725672702","https://openalex.org/W6729756640","https://openalex.org/W6733645847","https://openalex.org/W6734483310","https://openalex.org/W6734787559","https://openalex.org/W6736296761","https://openalex.org/W6736640963","https://openalex.org/W6739008639","https://openalex.org/W6742113789","https://openalex.org/W6744511767","https://openalex.org/W6748918544"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"High":[0],"performance":[1,174],"of":[2,42,105,175],"deep":[3],"neural":[4],"network":[5],"based":[6,125],"systems":[7],"have":[8,20,68,79],"attracted":[9],"many":[10],"applications":[11],"in":[12],"object":[13],"recognition":[14,84],"and":[15,31,37,67,129,166,186,210],"face":[16,83],"recognition.":[17],"However,":[18],"researchers":[19],"also":[21,150],"demonstrated":[22],"them":[23],"to":[24,28,34,144,171,197],"be":[25,35],"highly":[26],"sensitive":[27],"adversarial":[29,46,62,76,96,155,176,217],"perturbation":[30,47,156,177],"hence,":[32],"tend":[33],"unreliable":[36],"lack":[38],"robustness.":[39],"While":[40],"most":[41,104],"the":[43,61,106,142,173],"research":[44],"on":[45,49,71,82,112,126,183],"focuses":[48],"image":[50,215],"specific":[51],"attacks,":[52,97],"recently,":[53],"image-agnostic":[54,113,146],"Universal":[55],"perturbations":[56],"are":[57,101],"proposed":[58],"which":[59],"learn":[60],"pattern":[63],"over":[64],"training":[65],"distribution":[66],"broader":[69],"impact":[70],"real-world":[72],"security":[73],"applications.":[74],"Such":[75],"attacks":[77,89],"can":[78,90,205],"compounding":[80],"effect":[81],"where":[85],"these":[86],"visually":[87],"imperceptible":[88],"cause":[91],"mismatches.":[92],"To":[93],"defend":[94],"against":[95],"sophisticated":[98],"detection":[99,178,200,208],"approaches":[100,108,204],"prevalent":[102],"but":[103,122],"existing":[107],"do":[109],"not":[110,195],"focus":[111],"attacks.":[114],"In":[115],"this":[116],"paper,":[117],"we":[118],"present":[119,151],"a":[120,137],"simple":[121],"efficient":[123],"approach":[124],"pixel":[127],"values":[128],"Principal":[130],"Component":[131],"Analysis":[132],"as":[133,141],"features":[134],"coupled":[135],"with":[136],"Support":[138],"Vector":[139],"Machine":[140],"classifier,":[143],"detect":[145],"universal":[147],"perturbations.":[148],"We":[149],"evaluation":[152],"metrics,":[153],"namely":[154],"class":[157,162],"classification":[158,163,168],"error":[159,164,169,212],"rate,":[160,165,170],"original":[161],"average":[167],"estimate":[172],"algorithms.":[179],"The":[180],"experimental":[181],"results":[182],"multiple":[184],"databases":[185],"different":[187],"DNN":[188],"architectures":[189],"show":[190],"that":[191],"it":[192],"is":[193],"indeed":[194],"required":[196],"build":[198],"complex":[199],"algorithms;":[201],"rather":[202],"simpler":[203],"yield":[206],"higher":[207],"rates":[209,213],"lower":[211],"for":[214],"agnostic":[216],"perturbation.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-02-02T03:55:41.653505","created_date":"2025-10-10T00:00:00"}