Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection

• URL: http://arxiv.org/abs/2512.06363v1
• Date: Sat, 06 Dec 2025 09:34:39 GMT
• Title: Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection
• Authors: Jiabao Guo, Yadian Wang, Hui Ma, Yuhao Fu, Ju Jia, Hui Liu, Shengeng Tang, Lechao Cheng, Yunfeng Diao, Ajian Liu,
• Abstract summary: Real-world face recognition systems are vulnerable to both physical presentation attacks (PAs) and digital forgery attacks (DFs)<n>We propose a Spoofing-aware Prompt Learning for Unified Attack Detection (SPL-UAD) framework, which decouples optimization branches for physical and digital attacks in the prompt space.<n>Experiments on the large-scale UniAttackDataPlus dataset demonstrate that the proposed method achieves significant performance improvements in unified attack detection tasks.
• Score: 28.74960061024677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world face recognition systems are vulnerable to both physical presentation attacks (PAs) and digital forgery attacks (DFs). We aim to achieve comprehensive protection of biometric data by implementing a unified physical-digital defense framework with advanced detection. Existing approaches primarily employ CLIP with regularization constraints to enhance model generalization across both tasks. However, these methods suffer from conflicting optimization directions between physical and digital attack detection under same category prompt spaces. To overcome this limitation, we propose a Spoofing-aware Prompt Learning for Unified Attack Detection (SPL-UAD) framework, which decouples optimization branches for physical and digital attacks in the prompt space. Specifically, we construct a learnable parallel prompt branch enhanced with adaptive Spoofing Context Prompt Generation, enabling independent control of optimization for each attack type. Furthermore, we design a Cues-awareness Augmentation that leverages the dual-prompt mechanism to generate challenging sample mining tasks on data, significantly enhancing the model's robustness against unseen attack types. Extensive experiments on the large-scale UniAttackDataPlus dataset demonstrate that the proposed method achieves significant performance improvements in unified attack detection tasks.

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