Prior studies show that the key to face anti-spoofing lies in the subtle image patterns, termed “spoof trace,” e.g. , color distortion, 3D mask edge, and Moiré pattern. Spoof detection rooted on those spoof traces can improve not only the model's generalization but also the interpretability. Yet, it is a challenging task due to the diversity of spoof attacks and the lack of ground truth for spoof traces. In this work, we propose a novel adversarial learning framework to explicitly estimate the spoof related patterns for face anti-spoofing. Inspired by the physical process, spoof faces are disentangled into spoof traces and the live counterparts in two steps: additive step and inpainting step. This two-step modeling can effectively narrow down the searching space for adversarial learning of spoof trace. Based on the trace modeling, the disentangled spoof traces can be utilized to reversely construct new spoof faces, which is used as data augmentation to effectively tackle long-tail spoof types. In addition, we apply frequency-based image decomposition in both the input and disentangled traces to better reflect the low-level vision cues. Our approach demonstrates superior spoof detection performance on 3 testing scenarios: known attacks, unknown attacks, and open-set attacks. Meanwhile, it provides a visually-convincing estimation of the spoof traces. Source code and pre-trained models will be publicly available upon publication.

中文翻译：

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通用人脸反欺骗的欺骗跟踪解缠结

先前的研究表明，人脸反欺骗的关键在于微妙的图像模式，称为“欺骗痕迹”，例如，颜色失真、3D 掩模边缘和摩尔纹。基于这些欺骗痕迹的欺骗检测不仅可以提高模型的泛化能力，还可以提高可解释性。然而，由于恶搞攻击的多样性和恶搞痕迹缺乏真实性，这是一项具有挑战性的任务。在这项工作中，我们提出了一种新颖的对抗性学习框架来明确估计面部反欺骗的欺骗相关模式。受物理过程的启发，恶搞人脸分两步分解为恶搞痕迹和真实对应物：添加步骤和修复步骤。这种两步建模可以有效缩小欺骗痕迹对抗性学习的搜索空间。基于轨迹建模，可以利用解开的恶搞轨迹来反向构建新的恶搞面孔，用作数据增强以有效解决长尾欺骗类型。此外，我们在输入和分离轨迹中应用基于频率的图像分解，以更好地反映低级视觉线索。我们的方法在 3 个测试场景中展示了卓越的欺骗检测性能：已知攻击、未知攻击和开放集攻击。同时，它提供了对欺骗痕迹的视觉上令人信服的估计。源代码和预训练模型将在发布后公开提供。同时，它提供了对欺骗痕迹的视觉上令人信服的估计。源代码和预训练模型将在发布后公开提供。同时，它提供了对欺骗痕迹的视觉上令人信服的估计。源代码和预训练模型将在发布后公开提供。