{"id":"https://openalex.org/W4214870029","doi":"https://doi.org/10.1109/access.2022.3155870","title":"Open-Set Learning-Based Hologram Verification System Using Generative Adversarial Networks","display_name":"Open-Set Learning-Based Hologram Verification System Using Generative Adversarial Networks","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4214870029","doi":"https://doi.org/10.1109/access.2022.3155870"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3155870","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155870","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09724287.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09724287.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013994713","display_name":"Bet\u00fcl Ay","orcid":"https://orcid.org/0000-0002-3060-0432"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Betul Ay","raw_affiliation_strings":["Department of Computer Engineering, Firat University, Elazig, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-3060-0432","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Firat University, Elazig, Turkey","institution_ids":["https://openalex.org/I143396566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5013994713"],"corresponding_institution_ids":["https://openalex.org/I143396566"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6258,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.84364738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"10","issue":null,"first_page":"25114","last_page":"25124"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9994999766349792,"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":0.9994999766349792,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9848999977111816,"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/T11897","display_name":"Digital Holography and Microscopy","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8430823087692261},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7429274916648865},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7376855611801147},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6913966536521912},{"id":"https://openalex.org/keywords/holography","display_name":"Holography","score":0.5940134525299072},{"id":"https://openalex.org/keywords/open-set","display_name":"Open set","score":0.590647280216217},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5146904587745667},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5105587244033813},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5076435804367065},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.505587100982666},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4797890782356262},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4496937692165375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43994754552841187},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4206121563911438},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39378586411476135},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2694692313671112},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07304579019546509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8430823087692261},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7429274916648865},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7376855611801147},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6913966536521912},{"id":"https://openalex.org/C187590223","wikidata":"https://www.wikidata.org/wiki/Q527628","display_name":"Holography","level":2,"score":0.5940134525299072},{"id":"https://openalex.org/C42357961","wikidata":"https://www.wikidata.org/wiki/Q213363","display_name":"Open set","level":2,"score":0.590647280216217},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5146904587745667},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5105587244033813},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5076435804367065},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.505587100982666},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4797890782356262},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4496937692165375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43994754552841187},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4206121563911438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39378586411476135},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2694692313671112},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07304579019546509},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3155870","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155870","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09724287.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8be10fb72a2e43108fef404d21a0d612","is_oa":true,"landing_page_url":"https://doaj.org/article/8be10fb72a2e43108fef404d21a0d612","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 25114-25124 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3155870","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155870","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09724287.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4214870029.pdf","grobid_xml":"https://content.openalex.org/works/W4214870029.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W322671361","https://openalex.org/W1521410994","https://openalex.org/W1522301498","https://openalex.org/W1558778037","https://openalex.org/W2066087661","https://openalex.org/W2095705004","https://openalex.org/W2099471712","https://openalex.org/W2102605133","https://openalex.org/W2163147675","https://openalex.org/W2181762173","https://openalex.org/W2331128040","https://openalex.org/W2419597278","https://openalex.org/W2429883782","https://openalex.org/W2561599671","https://openalex.org/W2599129785","https://openalex.org/W2624918875","https://openalex.org/W2755118520","https://openalex.org/W2765811365","https://openalex.org/W2773971525","https://openalex.org/W2783748519","https://openalex.org/W2799709780","https://openalex.org/W2884471168","https://openalex.org/W2895752198","https://openalex.org/W2921155494","https://openalex.org/W2963470893","https://openalex.org/W2963684088","https://openalex.org/W2970434787","https://openalex.org/W2981637223","https://openalex.org/W3014586865","https://openalex.org/W3105939760","https://openalex.org/W3115462576","https://openalex.org/W3161130373","https://openalex.org/W4295274059","https://openalex.org/W6631190155","https://openalex.org/W6631277157","https://openalex.org/W6674330103","https://openalex.org/W6684191040","https://openalex.org/W6685352114","https://openalex.org/W6714138976","https://openalex.org/W6717177883","https://openalex.org/W6736155344","https://openalex.org/W6739625446","https://openalex.org/W6741832134","https://openalex.org/W6753655362","https://openalex.org/W6877159649"],"related_works":["https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4385421777","https://openalex.org/W3178813832","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2971552217","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4226298148"],"abstract_inverted_index":{"In":[0],"this":[1,65],"study,":[2],"we":[3,152],"address":[4],"the":[5,23,26,45,68,79,98,101,105,109,131,172,203,208],"hologram":[6,16,27,90,111,134,146],"authenticity":[7],"challenge":[8,63],"by":[9,38,47,126],"introducing":[10],"a":[11,31,40,138],"novel":[12],"deep-learning":[13],"based":[14],"end-to-end":[15],"verification":[17],"system.":[18],"The":[19,183,199],"system":[20,46,185,209],"ultimately":[21],"makes":[22],"decision":[24],"whether":[25],"image":[28],"captured":[29],"from":[30],"mobile":[32],"application":[33],"is":[34,94,114,120],"fake":[35,76,89],"or":[36,74],"not":[37,95],"employing":[39],"robust":[41],"classifier.":[42],"We":[43,128,165],"built":[44],"training":[48],"three":[49],"major":[50,62],"deep":[51],"networks;":[52],"generative":[53,157],"networks,":[54],"convolutional":[55,59,179],"networks":[56,159,181],"and":[57,92,168,174,191,195],"region-based":[58],"networks.":[60],"One":[61],"in":[64,108,137],"study":[66],"was":[67],"lack":[69],"of":[70,81,133,207],"negative":[71,106,163],"class":[72,107],"samples":[73],"so-called":[75],"holograms.":[77,103],"To":[78,144],"best":[80],"our":[82],"knowledge":[83],"there":[84],"are":[85],"no":[86],"publicly":[87],"available":[88],"datasets":[91],"it":[93,119],"clear":[96],"how":[97,122],"attackers":[99],"imitate":[100,124],"real":[102],"Therefore,":[104],"practical":[110],"classification":[112,135],"task":[113],"actually":[115],"\u201cunknown\u201d":[116],"class,":[117],"as":[118,136],"unknown":[121,211],"to":[123,141,150,161],"holograms":[125],"attackers.":[127],"hereby":[129],"consider":[130],"problem":[132],"similar":[139],"logic":[140],"open-set":[142,175,196],"recognition.":[143],"make":[145],"classifier":[147],"more":[148],"sensitive":[149],"forgery,":[151],"generate":[153],"synthetic":[154],"images":[155],"using":[156,176],"adversarial":[158],"(GANs)":[160],"represent":[162],"class.":[164],"conduct":[166],"extensive":[167],"comparative":[169],"experiments":[170],"on":[171],"closed-set":[173,194],"the-state-of-the-art":[177],"backbone":[178],"neural":[180],"(CNNs).":[182],"proposed":[184],"gives":[186],"an":[187],"impressive":[188],"accuracy":[189],"97.5%":[190],"79%":[192],"for":[193,210],"samples,":[197],"respectively.":[198],"reported":[200],"results":[201],"show":[202],"strong":[204],"generalization":[205],"performance":[206],"samples.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
