论文标题
在解开欺骗痕迹上，用于抗疾病
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
论文作者
论文摘要
先前的研究表明，面对抗散热器的关键在于微妙的图像模式，称为“欺骗痕迹”，例如颜色扭曲，3D蒙版边缘，Moire模式等。设计一个通用的反助剂模型来估计这些欺骗痕迹不仅可以改善欺骗检测的概括，而且还可以改善模型决策的解释性。然而，由于欺骗类型的多样性以及欺骗痕迹缺乏地面真理，这是一项艰巨的任务。这项工作设计了一种新颖的对抗学习框架，以将欺骗痕迹从输入面中解散为多个尺度上模式的分层组合。借助解开的欺骗痕迹，我们揭开了原始欺骗脸的现场直播，并在适当的几何校正后进一步综合了现实的新欺骗面。我们的方法表明，在可见的和看不见的欺骗场景上，同时提供了对欺骗痕迹的视觉估计，在可见和看不见的欺骗场景上表现出了出色的欺骗检测性能。代码可在https://github.com/yaojieliu/eccv20-stdn上找到。
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge, Moire pattern, and many others. Designing a generic anti-spoofing model to estimate those spoof traces can improve not only the generalization of the spoof detection, but also the interpretability of the model's decision. Yet, this is a challenging task due to the diversity of spoof types and the lack of ground truth in spoof traces. This work designs a novel adversarial learning framework to disentangle the spoof traces from input faces as a hierarchical combination of patterns at multiple scales. With the disentangled spoof traces, we unveil the live counterpart of the original spoof face, and further synthesize realistic new spoof faces after a proper geometric correction. Our method demonstrates superior spoof detection performance on both seen and unseen spoof scenarios while providing visually convincing estimation of spoof traces. Code is available at https://github.com/yaojieliu/ECCV20-STDN.
