Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

• URL: http://arxiv.org/abs/2505.09484v1
• Date: Wed, 14 May 2025 15:36:44 GMT
• Title: Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
• Authors: Yingjie Ma, Xun Lin, Zitong Yu, Xin Liu, Xiaochen Yuan, Weicheng Xie, Linlin Shen,
• Abstract summary: Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios.<n>We introduce the textbfMultitextbfmodal textbfDenoising and textbfAlignment (textbfMMDA) framework.<n>By leveraging the zero-shot generalization capability of CLIP, the MMDA framework effectively suppresses noise in multimodal data.
• Score: 47.24147617685829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillance. Current multimodal FAS methods often struggle with effective generalization, mainly due to modality-specific biases and domain shifts. To address these challenges, we introduce the \textbf{M}ulti\textbf{m}odal \textbf{D}enoising and \textbf{A}lignment (\textbf{MMDA}) framework. By leveraging the zero-shot generalization capability of CLIP, the MMDA framework effectively suppresses noise in multimodal data through denoising and alignment mechanisms, thereby significantly enhancing the generalization performance of cross-modal alignment. The \textbf{M}odality-\textbf{D}omain Joint \textbf{D}ifferential \textbf{A}ttention (\textbf{MD2A}) module in MMDA concurrently mitigates the impacts of domain and modality noise by refining the attention mechanism based on extracted common noise features. Furthermore, the \textbf{R}epresentation \textbf{S}pace \textbf{S}oft (\textbf{RS2}) Alignment strategy utilizes the pre-trained CLIP model to align multi-domain multimodal data into a generalized representation space in a flexible manner, preserving intricate representations and enhancing the model's adaptability to various unseen conditions. We also design a \textbf{U}-shaped \textbf{D}ual \textbf{S}pace \textbf{A}daptation (\textbf{U-DSA}) module to enhance the adaptability of representations while maintaining generalization performance. These improvements not only enhance the framework's generalization capabilities but also boost its ability to represent complex representations. Our experimental results on four benchmark datasets under different evaluation protocols demonstrate that the MMDA framework outperforms existing state-of-the-art methods in terms of cross-domain generalization and multimodal detection accuracy. The code will be released soon.

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