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Anomaly Detection with Transformers in Face Anti-spoofing

Abduh, Latifah; Omar, Luma; Ivrissimtzis, Ioannis

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Authors

Latifah Abduh latifah.a.abduh@durham.ac.uk
PGR Student Doctor of Philosophy

Luma Omar



Abstract

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing, and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing.

Citation

Abduh, L., Omar, L., & Ivrissimtzis, I. (2023). Anomaly Detection with Transformers in Face Anti-spoofing. . https://doi.org/10.24132/JWSCG.2023.10

Conference Name WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023
Conference Location Plzen, Czech Republic
Start Date May 15, 2023
End Date May 19, 2023
Acceptance Date Apr 21, 2023
Publication Date 2023
Deposit Date Jun 15, 2023
Publicly Available Date Jul 27, 2023
Publisher WSCG
Volume 30
Series ISSN 1213-6972
DOI https://doi.org/10.24132/JWSCG.2023.10
Public URL https://durham-repository.worktribe.com/output/1133859
Publisher URL http://wscg.zcu.cz/DL/wscg_DL.htm

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