Abstract
Face recognition systems are vulnerable to physical attacks (e.g., printed photos) and digital threats (e.g., DeepFake), which are currently being studied as independent visual tasks, such as Face Anti-Spoofing and Forgery Detection. The inherent differences among various attack types present significant challenges in identifying a common feature space, making it difficult to develop a unified framework for detecting data from both attack modalities simultaneously. Inspired by the efficacy of Mixture-of-Experts (MoE) in learning across diverse domains, we explore utilizing multiple experts to learn the distinct features of various attack types. However, the feature distributions of physical and digital attacks overlap and differ. This suggests that relying solely on distinct experts to learn the unique features of each attack type may overlook shared knowledge between them. To address these issues, we propose SUEDE, the Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement. SUEDE combines a shared expert (always activated) to capture common features for both attack types and multiple routed experts (selectively activated) for specific attack types. Further, we integrate CLIP as the base network to ensure the shared expert benefits from prior visual knowledge and align visual-text representations in a unified space. Extensive results demonstrate SUEDE achieves superior performance compared to state-of-the-art unified detection methods.
Abstract (translated)
面部识别系统容易受到物理攻击（如打印的照片）和数字威胁（如DeepFake）的影响，目前这些独立的视觉任务正在被分别研究，例如防欺骗检测和伪造物检测。不同类型的攻击之间存在的固有差异为建立一个能够同时处理两种模式攻击数据的共同特征空间带来了巨大挑战，从而难以开发出统一框架来应对这些问题。受混合专家（MoE）在跨多样域学习中的有效性的启发，我们探索使用多个专家来学习各种攻击类型的独特特性。然而，物理和数字攻击的特征分布相互重叠但又有所不同。这表明，仅仅依靠不同的专家来学习每种特定类型的独特特征可能会忽略它们之间共享的知识。为了应对这些挑战，我们提出了SUEDE（Physical-Digital Face Attack Detection Enhancement的共同统一专家系统）。SUEDE结合了一个共享专家（始终激活）用于捕捉两种攻击类型之间的通用特性，并且多个路由专家（选择性激活）来处理特定类型的攻击。此外，我们将CLIP作为基础网络集成进来，以确保共享专家能够从先前的视觉知识中受益并使视觉-文本表示在统一的空间内对齐。广泛的实验结果表明，SUEDE相较于现有的统一检测方法表现出更优越的性能。
URL
https://arxiv.org/abs/2504.04818
