Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910929712578560 |
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| author | Jiang, Fangling Li, Qi Wang, Weining Shen, Wei Liu, Bing Sun, Zhenan |
| author_facet | Jiang, Fangling Li, Qi Wang, Weining Shen, Wei Liu, Bing Sun, Zhenan |
| contents | Face anti-spoofing is a critical technology for ensuring the security of face recognition systems. However, its ability to generalize across diverse scenarios remains a significant challenge. In this paper, we attribute the limited generalization ability to two key factors: covariate shift, which arises from external data collection variations, and semantic shift, which results from substantial differences in emerging attack types. To address both challenges, we propose a novel approach for learning unknown spoof prompts, relying solely on real face images from a single source domain. Our method generates textual prompts for real faces and potential unknown spoof attacks by leveraging the general knowledge embedded in vision-language models, thereby enhancing the model's ability to generalize to unseen target domains. Specifically, we introduce a diverse spoof prompt optimization framework to learn effective prompts. This framework constrains unknown spoof prompts within a relaxed prior knowledge space while maximizing their distance from real face images. Moreover, it enforces semantic independence among different spoof prompts to capture a broad range of spoof patterns. Experimental results on nine datasets demonstrate that the learned prompts effectively transfer the knowledge of vision-language models, enabling state-of-the-art generalization ability against diverse unknown attack types across unseen target domains without using any spoof face images. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_03611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images Jiang, Fangling Li, Qi Wang, Weining Shen, Wei Liu, Bing Sun, Zhenan Computer Vision and Pattern Recognition Face anti-spoofing is a critical technology for ensuring the security of face recognition systems. However, its ability to generalize across diverse scenarios remains a significant challenge. In this paper, we attribute the limited generalization ability to two key factors: covariate shift, which arises from external data collection variations, and semantic shift, which results from substantial differences in emerging attack types. To address both challenges, we propose a novel approach for learning unknown spoof prompts, relying solely on real face images from a single source domain. Our method generates textual prompts for real faces and potential unknown spoof attacks by leveraging the general knowledge embedded in vision-language models, thereby enhancing the model's ability to generalize to unseen target domains. Specifically, we introduce a diverse spoof prompt optimization framework to learn effective prompts. This framework constrains unknown spoof prompts within a relaxed prior knowledge space while maximizing their distance from real face images. Moreover, it enforces semantic independence among different spoof prompts to capture a broad range of spoof patterns. Experimental results on nine datasets demonstrate that the learned prompts effectively transfer the knowledge of vision-language models, enabling state-of-the-art generalization ability against diverse unknown attack types across unseen target domains without using any spoof face images. |
| title | Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.03611 |