Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing

• URL: http://arxiv.org/abs/2503.22984v1
• Date: Sat, 29 Mar 2025 06:10:34 GMT
• Title: Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing
• Authors: Zhuowei Li, Tianchen Zhao, Xiang Xu, Zheng Zhang, Zhihua Li, Xuanbai Chen, Qin Zhang, Alessandro Bergamo, Anil K. Jain, Yifan Xing,
• Abstract summary: 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.<n>Specifically, we develop a prototype-based base model and an optimal transport-guided adaptor.<n>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.
• Score: 58.56017169759816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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