SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining

• URL: http://arxiv.org/abs/2503.19982v1
• Date: Tue, 25 Mar 2025 18:09:06 GMT
• Title: SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
• Authors: Pei-Kai Huang, Jun-Xiong Chong, Cheng-Hsuan Chiang, Tzu-Hsien Chen, Tyng-Luh Liu, Chiou-Ting Hsu,
• Abstract summary: Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems.<n>One-class FAS focuses on learning intrinsic liveness features solely from live training images to differentiate between live and spoof faces.<n>We propose a novel framework called Spoof-aware one-class face anti-spoofing with Language Image Pretraining (SLIP)
• Score: 12.144338080864127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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