Spoofing Attack Detection in Face Recognition System Using Vision Transformer with Patch-wise Data Augmentation

Kota Watanabe, Koichi Ito, Takafumi Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spoofing attacks are a serious threat to face recognition systems by malicious third parties since face images can be easily collected from the Internet. In this paper, we propose a spoofing attack detection method using Vision Transformer (ViT), which extracts features based on patches to extract fine features in a face image. We also propose a patch-wise data augmentation to improve the detection accuracy of spoofing attacks. We demonstrate the effectiveness of the proposed method through accuracy evaluation experiments using public datasets.

Original languageEnglish
Title of host publicationProceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1561-1565
Number of pages5
ISBN (Electronic)9786165904773
DOIs
Publication statusPublished - 2022
Event2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022 - Chiang Mai, Thailand
Duration: 2022 Nov 72022 Nov 10

Publication series

NameProceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022

Conference

Conference2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Country/TerritoryThailand
CityChiang Mai
Period22/11/722/11/10

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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