Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance.

翻译：人脸反欺骗（FAS）旨在检测通过呈现伪造人脸入侵人脸识别系统的恶意行为。现有先进FAS技术主要依赖深度学习模型，但训练数据与测试数据分布不同导致的域偏移问题往往限制了其跨域泛化能力。本研究在高效参数迁移学习（EPTL）范式下开发了一种泛化FAS方法，通过适配预训练视觉Transformer模型完成FAS任务。训练过程中，适配器模块被插入预训练ViT模型，仅更新适配器参数而保持其他预训练参数固定。我们发现传统vanilla适配器基于线性层存在局限性，缺乏欺骗感知的归纳偏置，从而制约了跨域泛化。为解决这一局限并实现跨域泛化FAS，我们提出新颖的统计适配器（S-Adapter），通过从局部标记直方图中收集局部判别性与统计信息。为进一步提升统计标记的泛化能力，我们提出新颖的标记风格正则化（TSR），通过对跨域标记提取的Gram矩阵进行正则化以降低域风格差异。实验结果表明，本文提出的S-Adapter与TSR在零样本和小样本跨域测试中均展现出显著优势，在多个基准测试上超越现有先进方法。论文接收后将公开源代码。