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.
Abstract (translated)
现实世界中的面部识别系统容易受到物理展示攻击（PA）和数字伪造攻击（DF）的威胁。我们的目标是通过实施一个具有高级检测功能的统一物理-数字防御框架来实现生物特征数据的全面保护。现有的方法主要采用带有正则化约束条件的CLIP，以增强模型在两类任务中的泛化能力。然而，这些方法在同类别提示空间中存在针对物理和数字攻击检测优化方向冲突的问题。 为了解决这一局限性，我们提出了一个名为“基于伪造意识提示学习的一体化攻击检测框架”（SPL-UAD），该框架在提示空间中解耦了对物理和数字攻击的优化分支。具体而言，我们构建了一个可以通过自适应伪造上下文提示生成来增强的可学习并行提示分支，从而能够独立控制每种攻击类型的优化过程。 此外，我们设计了一种基于线索感知的数据增强机制，利用双提示机制在数据上生成挑战性的样本挖掘任务，显著提高了模型对未见过的攻击类型时的鲁棒性。在大规模UniAttackDataPlus数据集上的广泛实验表明，所提出的方法在统一攻击检测任务中取得了显著的性能改进。
URL
https://arxiv.org/abs/2512.06363
