IRonMask: Modular architecture for protecting deep face template
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, S. | - |
dc.contributor.author | Jeong, Y. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Lee, H.T. | - |
dc.contributor.author | Seo, J.H. | - |
dc.date.accessioned | 2023-03-08T10:51:24Z | - |
dc.date.available | 2023-03-08T10:51:24Z | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62365 | - |
dc.description.abstract | Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed. In this paper, we present a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric. We circumvent the need for binarization, which is the main cause of performance degradation in most existing face template protections, by proposing a new real-valued error-correcting-code that is compatible with real-valued templates and can therefore, minimize performance degradation. We evaluate the efficacy of IronMask by extensive experiments on two face recognitions, ArcFace and CosFace with three datasets, CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results, IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate (FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with CosFace, while providing at least 115-bit security against known attacks. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | IRonMask: Modular architecture for protecting deep face template | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/CVPR46437.2021.01586 | - |
dc.identifier.bibliographicCitation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 16120 - 16129 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000742075006033 | - |
dc.identifier.scopusid | 2-s2.0-85118519917 | - |
dc.citation.endPage | 16129 | - |
dc.citation.startPage | 16120 | - |
dc.citation.title | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.type.docType | Proceedings Paper | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.