Abstract
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.
Abstract (translated)
人脸防伪技术是确保人脸识别系统安全性的关键。然而，其在不同场景下的泛化能力仍然是一个重大挑战。在这篇论文中，我们将有限的泛化能力归因于两个重要因素：协变量变化（covariate shift），这是由于外部数据采集差异引起的；以及语义变化（semantic shift），这是由新兴攻击类型的重大区别所导致的。 为了解决这两个挑战，我们提出了一种新的方法来学习未知的防伪提示，仅依赖单个源域的真实人脸图像。我们的方法通过利用视觉-语言模型中嵌入的一般知识生成真实面部和潜在未知伪造攻击的文字提示，从而提高模型在未见目标领域中的泛化能力。 具体来说，我们引入了一个多样化的伪造提示优化框架来学习有效的提示。此框架将未知的伪造提示约束在一个放松的先验知识空间内，同时最大化其与真实人脸图像的距离。此外，它还强制不同的伪造提示之间保持语义独立性，以捕捉广泛的伪造模式。 在九个数据集上的实验结果表明，所学得的提示有效地转移了视觉-语言模型的知识，在没有使用任何伪造面部图像的情况下，能够使模型对未见目标领域中各种未知攻击类型具有最先进的泛化能力。
URL
https://arxiv.org/abs/2505.03611
