MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Y. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Lee, J. | - |
dc.contributor.author | Hong, M. | - |
dc.contributor.author | Hwang, S. | - |
dc.contributor.author | Choi, Jongwon | - |
dc.date.accessioned | 2023-03-08T09:19:05Z | - |
dc.date.available | 2023-03-08T09:19:05Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61735 | - |
dc.description.abstract | In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent such harm, we propose an anti-spoofing method using the pairs of RGB images and depth maps provided by the mobile camera with a time-of-fight sensor. When images are recaptured on display screens, various patterns differing by the screens as known as the moiré patterns can be also captured in spoof images. These patterns lead the anti-spoofing model to be overfitted and unable to detect spoof images recaptured on unseen media. To avoid the issue, we build a novel representation model composed of two embedding models, which can be trained without considering the recaptured images. Also, we newly introduce mToF dataset, the largest and most diverse object anti-spoofing dataset, and the first to utilize the time-of-flight (ToF) data. Experimental results confirm that our model achieves robust generalization even across unseen domains. © 2022 IEEE. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/WACV51458.2022.00305 | - |
dc.identifier.bibliographicCitation | Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, pp 2997 - 3006 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000800471203007 | - |
dc.identifier.scopusid | 2-s2.0-85126091596 | - |
dc.citation.endPage | 3006 | - |
dc.citation.startPage | 2997 | - |
dc.citation.title | Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Security/Surveillance | - |
dc.subject.keywordAuthor | Vision Systems and Applications 3D Computer Vision | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
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.