{"id":"https://openalex.org/W4308014785","doi":"https://doi.org/10.48550/arxiv.2211.00098","title":"Synthetic ID Card Image Generation for Improving Presentation Attack Detection","display_name":"Synthetic ID Card Image Generation for Improving Presentation Attack Detection","publication_year":2022,"publication_date":"2022-10-31","ids":{"openalex":"https://openalex.org/W4308014785","doi":"https://doi.org/10.48550/arxiv.2211.00098"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.00098","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.00098","pdf_url":"https://arxiv.org/pdf/2211.00098","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.00098","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049786310","display_name":"Daniel Benalcazar","orcid":"https://orcid.org/0000-0002-2030-9449"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benalcazar, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017736954","display_name":"Juan Tapia","orcid":"https://orcid.org/0000-0001-9159-4075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tapia, Juan E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100746285","display_name":"Sebasti\u00e1n Gonz\u00e1lez","orcid":"https://orcid.org/0000-0001-9545-167X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gonzalez, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017716310","display_name":"Christoph Busch","orcid":"https://orcid.org/0000-0002-9159-2923"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Busch, Christoph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9994000196456909,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9994000196456909,"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/T11448","display_name":"Face recognition and analysis","score":0.9975000023841858,"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/T10828","display_name":"Biometric Identification and Security","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7989785671234131},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.6990933418273926},{"id":"https://openalex.org/keywords/presentation","display_name":"Presentation (obstetrics)","score":0.5643552541732788},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.53805011510849},{"id":"https://openalex.org/keywords/gadget","display_name":"Gadget","score":0.4725092351436615},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4189887046813965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38225698471069336},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.3500133752822876}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7989785671234131},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.6990933418273926},{"id":"https://openalex.org/C2777601897","wikidata":"https://www.wikidata.org/wiki/Q3409113","display_name":"Presentation (obstetrics)","level":2,"score":0.5643552541732788},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.53805011510849},{"id":"https://openalex.org/C119770614","wikidata":"https://www.wikidata.org/wiki/Q5516347","display_name":"Gadget","level":2,"score":0.4725092351436615},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4189887046813965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38225698471069336},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.3500133752822876},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2211.00098","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.00098","pdf_url":"https://arxiv.org/pdf/2211.00098","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2211.00098","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.00098","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2211.00098","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.00098","pdf_url":"https://arxiv.org/pdf/2211.00098","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4252293060","https://openalex.org/W4212943538","https://openalex.org/W2475971442","https://openalex.org/W4312756098","https://openalex.org/W3194202522","https://openalex.org/W4319586723","https://openalex.org/W2206440698","https://openalex.org/W4389285307","https://openalex.org/W2495546752","https://openalex.org/W2011311868"],"abstract_inverted_index":{"Currently,":[0],"it":[1],"is":[2,115],"ever":[3],"more":[4],"common":[5],"to":[6,20,52,78,90,128],"access":[7],"online":[8],"services":[9],"for":[10,61,110,122,164,179],"activities":[11],"which":[12],"formerly":[13],"required":[14],"physical":[15],"attendance.":[16],"From":[17],"banking":[18],"operations":[19],"visa":[21],"applications,":[22],"a":[23,102,173],"significant":[24],"number":[25,107],"of":[26,35,43,58,97,108,132,177],"processes":[27],"have":[28,82],"been":[29,83],"digitised,":[30],"especially":[31],"since":[32],"the":[33,36,44,47,55,86,94,105,130,165,180],"advent":[34],"COVID-19":[37],"pandemic,":[38],"requiring":[39],"remote":[40,59],"biometric":[41],"authentication":[42],"user.":[45],"On":[46],"downside,":[48],"some":[49],"subjects":[50],"intend":[51],"interfere":[53],"with":[54,104,156],"normal":[56],"operation":[57],"systems":[60],"personal":[62,98],"profit":[63],"by":[64],"using":[65],"fake":[66],"identity":[67,99],"documents,":[68,100],"such":[69,80],"as":[70],"passports":[71],"and":[72,93,144,172],"ID":[73,125],"cards.":[74],"Deep":[75],"learning":[76],"solutions":[77],"detect":[79],"frauds":[81],"presented":[84],"in":[85,162,175],"literature.":[87],"However,":[88],"due":[89],"privacy":[91],"concerns":[92],"sensitive":[95],"nature":[96],"developing":[101],"dataset":[103],"necessary":[106],"examples":[109],"training":[111,135],"deep":[112],"neural":[113],"networks":[114],"challenging.":[116],"This":[117],"work":[118],"explores":[119],"three":[120],"methods":[121,139],"synthetically":[123],"generating":[124],"card":[126],"images":[127,158],"increase":[129],"amount":[131],"data":[133],"while":[134],"fraud-detection":[136],"networks.":[137],"These":[138],"include":[140],"computer":[141],"vision":[142],"algorithms":[143],"Generative":[145],"Adversarial":[146],"Networks.":[147],"Our":[148],"results":[149],"indicate":[150],"that":[151],"databases":[152],"can":[153],"be":[154],"supplemented":[155],"synthetic":[157],"without":[159],"any":[160],"loss":[161,174],"performance":[163,176],"print/scan":[166],"Presentation":[167],"Attack":[168],"Instrument":[169],"Species":[170],"(PAIS)":[171],"1%":[178],"screen":[181],"capture":[182],"PAIS.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2022-11-07T00:00:00"}
