Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofing
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911139236937728 |
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| author | Zhang, Haoyuan Zhu, Xiangyu Gao, Li Pan, Jiawei Pang, Kai Zhao, Guoying Lei, Zhen |
| author_facet | Zhang, Haoyuan Zhu, Xiangyu Gao, Li Pan, Jiawei Pang, Kai Zhao, Guoying Lei, Zhen |
| contents | With the rapid growth usage of face recognition in people's daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the explanation to answer "why it is fake". Such a system undermines trustworthiness and causes user confusion, as it denies their requests without providing any explanations. In this paper, we incorporate XAI into face anti-spoofing and propose a new problem termed X-FAS (eXplainable Face Anti-Spoofing) empowering face anti-spoofing models to provide an explanation. We propose SPTD (SPoof Trace Discovery), an X-FAS method which can discover spoof concepts and provide reliable explanations on the basis of discovered concepts. To evaluate the quality of X-FAS methods, we propose an X-FAS benchmark with annotated spoof traces by experts. We analyze SPTD explanations on face anti-spoofing dataset and compare SPTD quantitatively and qualitatively with previous XAI methods on proposed X-FAS benchmark. Experimental results demonstrate SPTD's ability to generate reliable explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofing Zhang, Haoyuan Zhu, Xiangyu Gao, Li Pan, Jiawei Pang, Kai Zhao, Guoying Lei, Zhen Computer Vision and Pattern Recognition Artificial Intelligence With the rapid growth usage of face recognition in people's daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the explanation to answer "why it is fake". Such a system undermines trustworthiness and causes user confusion, as it denies their requests without providing any explanations. In this paper, we incorporate XAI into face anti-spoofing and propose a new problem termed X-FAS (eXplainable Face Anti-Spoofing) empowering face anti-spoofing models to provide an explanation. We propose SPTD (SPoof Trace Discovery), an X-FAS method which can discover spoof concepts and provide reliable explanations on the basis of discovered concepts. To evaluate the quality of X-FAS methods, we propose an X-FAS benchmark with annotated spoof traces by experts. We analyze SPTD explanations on face anti-spoofing dataset and compare SPTD quantitatively and qualitatively with previous XAI methods on proposed X-FAS benchmark. Experimental results demonstrate SPTD's ability to generate reliable explanations. |
| title | Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.17541 |