Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910898957844480 |
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| author | Li, Zhuowei Zhao, Tianchen Xu, Xiang Zhang, Zheng Li, Zhihua Chen, Xuanbai Zhang, Qin Bergamo, Alessandro Jain, Anil K. Xing, Yifan |
| author_facet | Li, Zhuowei Zhao, Tianchen Xu, Xiang Zhang, Zheng Li, Zhihua Chen, Xuanbai Zhang, Qin Bergamo, Alessandro Jain, Anil K. Xing, Yifan |
| contents | Developing a face anti-spoofing model that meets the security requirements of clients worldwide is challenging due to the domain gap between training datasets and diverse end-user test data. Moreover, for security and privacy reasons, it is undesirable for clients to share a large amount of their face data with service providers. In this work, we introduce a novel method in which the face anti-spoofing model can be adapted by the client itself to a target domain at test time using only a small sample of data while keeping model parameters and training data inaccessible to the client. Specifically, we develop a prototype-based base model and an optimal transport-guided adaptor that enables adaptation in either a lightweight training or training-free fashion, without updating base model's parameters. Furthermore, we propose geodesic mixup, an optimal transport-based synthesis method that generates augmented training data along the geodesic path between source prototypes and target data distribution. This allows training a lightweight classifier to effectively adapt to target-specific characteristics while retaining essential knowledge learned from the source domain. In cross-domain and cross-attack settings, compared with recent methods, our method achieves average relative improvements of 19.17% in HTER and 8.58% in AUC, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing Li, Zhuowei Zhao, Tianchen Xu, Xiang Zhang, Zheng Li, Zhihua Chen, Xuanbai Zhang, Qin Bergamo, Alessandro Jain, Anil K. Xing, Yifan Computer Vision and Pattern Recognition I.5.4; I.2.10; I.4.8; I.2.6; C.3 Developing a face anti-spoofing model that meets the security requirements of clients worldwide is challenging due to the domain gap between training datasets and diverse end-user test data. Moreover, for security and privacy reasons, it is undesirable for clients to share a large amount of their face data with service providers. In this work, we introduce a novel method in which the face anti-spoofing model can be adapted by the client itself to a target domain at test time using only a small sample of data while keeping model parameters and training data inaccessible to the client. Specifically, we develop a prototype-based base model and an optimal transport-guided adaptor that enables adaptation in either a lightweight training or training-free fashion, without updating base model's parameters. Furthermore, we propose geodesic mixup, an optimal transport-based synthesis method that generates augmented training data along the geodesic path between source prototypes and target data distribution. This allows training a lightweight classifier to effectively adapt to target-specific characteristics while retaining essential knowledge learned from the source domain. In cross-domain and cross-attack settings, compared with recent methods, our method achieves average relative improvements of 19.17% in HTER and 8.58% in AUC, respectively. |
| title | Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing |
| topic | Computer Vision and Pattern Recognition I.5.4; I.2.10; I.4.8; I.2.6; C.3 |
| url | https://arxiv.org/abs/2503.22984 |