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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.02116 |
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| _version_ | 1866929326900903936 |
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| author | Chen, Jiawei Yang, Xiao Yin, Heng Ma, Mingzhi Chen, Bihui Peng, Jianteng Guo, Yandong Yin, Zhaoxia Su, Hang |
| author_facet | Chen, Jiawei Yang, Xiao Yin, Heng Ma, Mingzhi Chen, Bihui Peng, Jianteng Guo, Yandong Yin, Zhaoxia Su, Hang |
| contents | Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in this domain, the ability of even the most state-of-the-art methods to defend against adversarial examples remains elusive. While several adversarial defense strategies have been proposed, they typically suffer from constrained practicability due to inevitable trade-offs between universality, effectiveness, and efficiency. To overcome these challenges, we thoroughly delve into the coupled relationship between adversarial detection and face anti-spoofing. Based on this, we propose a robust face anti-spoofing framework, namely AdvFAS, that leverages two coupled scores to accurately distinguish between correctly detected and wrongly detected face images. Extensive experiments demonstrate the effectiveness of our framework in a variety of settings, including different attacks, datasets, and backbones, meanwhile enjoying high accuracy on clean examples. Moreover, we successfully apply the proposed method to detect real-world adversarial examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02116 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AdvFAS: A robust face anti-spoofing framework against adversarial examples Chen, Jiawei Yang, Xiao Yin, Heng Ma, Mingzhi Chen, Bihui Peng, Jianteng Guo, Yandong Yin, Zhaoxia Su, Hang Computer Vision and Pattern Recognition Artificial Intelligence Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in this domain, the ability of even the most state-of-the-art methods to defend against adversarial examples remains elusive. While several adversarial defense strategies have been proposed, they typically suffer from constrained practicability due to inevitable trade-offs between universality, effectiveness, and efficiency. To overcome these challenges, we thoroughly delve into the coupled relationship between adversarial detection and face anti-spoofing. Based on this, we propose a robust face anti-spoofing framework, namely AdvFAS, that leverages two coupled scores to accurately distinguish between correctly detected and wrongly detected face images. Extensive experiments demonstrate the effectiveness of our framework in a variety of settings, including different attacks, datasets, and backbones, meanwhile enjoying high accuracy on clean examples. Moreover, we successfully apply the proposed method to detect real-world adversarial examples. |
| title | AdvFAS: A robust face anti-spoofing framework against adversarial examples |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2308.02116 |