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dc.contributor.author | Markham, Reuben P.![]() |
es_ES |
dc.contributor.author | Espín López, Juan M.![]() |
es_ES |
dc.contributor.author | Nieto-Hidalgo, Mario![]() |
es_ES |
dc.contributor.author | Tapia, Juan E.![]() |
es_ES |
dc.date.accessioned | 2024-06-18T18:03:21Z | |
dc.date.available | 2024-06-18T18:03:21Z | |
dc.date.issued | 2024 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205261 | |
dc.description.abstract | [EN] The accurate detection of ID card Presentation Attacks (PA) is becoming increasingly important due to the rising number of online/remote services that require the presentation of digital photographs of ID cards for digital onboarding or authentication. Furthermore, cybercriminals are continuously searching for innovative ways to fool authentication systems to gain unauthorized access to these services. Although advances in neural network design and training have pushed image classification to the state of the art, one of the main challenges faced by the development of fraud detection systems is the curation of representative datasets for training and evaluation. The handcrafted creation of representative presentation attack samples often requires expertise and is very time-consuming, thus an automatic process of obtaining high-quality data is highly desirable. This work explores ID card Presentation Attack Instruments (PAI) in order to improve the generation of samples with four Generative Adversarial Networks (GANs) based image translation models and analyses the effectiveness of the generated data for training fraud detection systems. Using open-source data, we show that synthetic attack presentations are an adequate complement for additional real attack presentations, where we obtain an EER performance increase of 0.63 % points for print attacks and a loss of 0.29 % for screen capture attacks. | es_ES |
dc.description.sponsorship | This work was supported in part by the European Union (EU) Next-Generation, Plan de Recuperación, Transformación y Resiliencia through Convocatorias de Ayudas 2021, Proyecto Red.es, under Grant C005/21-ED; and in part by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity (ATHENE). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation.ispartof | IEEE Access | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Biometrics | es_ES |
dc.subject | Synthetic images | es_ES |
dc.subject | Remote verification | es_ES |
dc.subject | Presentation attack detection | es_ES |
dc.subject | ID card | es_ES |
dc.title | Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1109/ACCESS.2024.3397190 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC//C005%2F21-ED/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Markham, RP.; Espín López, JM.; Nieto-Hidalgo, M.; Tapia, JE. (2024). Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer. IEEE Access. 12:68573-68585. https://doi.org/10.1109/ACCESS.2024.3397190 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/ACCESS.2024.3397190 | es_ES |
dc.description.upvformatpinicio | 68573 | es_ES |
dc.description.upvformatpfin | 68585 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.identifier.eissn | 2169-3536 | es_ES |
dc.relation.pasarela | S\520285 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Bundesministerium für Bildung und Forschung, Alemania | es_ES |