Abstract
Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems. With advancements in vision-language pretrained (VLP) models, recent two-class FAS techniques have leveraged the advantages of using VLP guidance, while this potential remains unexplored in one-class FAS methods. The one-class FAS focuses on learning intrinsic liveness features solely from live training images to differentiate between live and spoof faces. However, the lack of spoof training data can lead one-class FAS models to inadvertently incorporate domain information irrelevant to the live/spoof distinction (e.g., facial content), causing performance degradation when tested with a new application domain. To address this issue, we propose a novel framework called Spoof-aware one-class face anti-spoofing with Language Image Pretraining (SLIP). Given that live faces should ideally not be obscured by any spoof-attack-related objects (e.g., paper, or masks) and are assumed to yield zero spoof cue maps, we first propose an effective language-guided spoof cue map estimation to enhance one-class FAS models by simulating whether the underlying faces are covered by attack-related objects and generating corresponding nonzero spoof cue maps. Next, we introduce a novel prompt-driven liveness feature disentanglement to alleviate live/spoof-irrelative domain variations by disentangling live/spoof-relevant and domain-dependent information. Finally, we design an effective augmentation strategy by fusing latent features from live images and spoof prompts to generate spoof-like image features and thus diversify latent spoof features to facilitate the learning of one-class FAS. Our extensive experiments and ablation studies support that SLIP consistently outperforms previous one-class FAS methods.
Abstract (translated)
面部防伪（FAS）在确保人脸识别系统的安全性和可靠性方面发挥着至关重要的作用。随着视觉-语言预训练模型的发展，最近的两类面部防伪技术已经利用了使用VLP指导的优势，而这种潜力尚未在单类面部防伪方法中得到充分探索。单类FAS专注于仅从活体训练图像学习内在的真实特征来区分真实面孔和伪造面孔。然而，在缺乏伪造数据的情况下，单类FAS模型可能会无意中包含与真伪无关的领域信息（例如，面部内容），这在测试新的应用领域时会导致性能下降。 为了解决这个问题，我们提出了一种新颖的框架，称为基于语言图像预训练的防伪感知单类脸部防伪（SLIP）。鉴于真实面孔不应被任何与伪造攻击相关的物体（如纸张或面具）遮挡，并且这些情况应产生零伪造提示图，我们首先提出了一个有效的语言引导的伪造提示图估计方法来增强单类FAS模型。该方法通过模拟人脸是否被攻击相关对象覆盖并生成相应的非零伪造提示图来进行。 接下来，我们引入了一种新颖的提示驱动的真实特征分离方法，以减轻与真伪无关的领域变化，通过分离相关的和依赖领域的信息来实现这一目标。 最后，我们设计了一个有效的增强策略，通过融合来自真实图像和伪造提示的潜在特性生成类似伪造的图像特性，并从而多样化潜在的伪造特征，以便更好地学习单类FAS模型。 我们的广泛实验和消融研究表明，SLIP始终优于以前的单类面部防伪方法。
URL
https://arxiv.org/abs/2503.19982
