Face anti-spoofing (a.k.a presentation attack detection) has drawn growing
attention due to the high-security demand in face authentication systems.
Existing CNN-based approaches usually well recognize the spoofing faces when
training and testing spoofing samples display similar patterns, but their
performance would drop drastically on testing spoofing faces of unseen scenes.
In this paper, we try to boost the generalizability and applicability of these
methods by designing a CNN model with two major novelties. First, we propose a
simple yet effective Total Pairwise Confusion (TPC) loss for CNN training,
which enhances the generalizability of the learned Presentation Attack (PA)
representations. Secondly, we incorporate a Fast Domain Adaptation (FDA)
component into the CNN model to alleviate negative effects brought by domain
changes. Besides, our proposed model, which is named Generalizable Face
Authentication CNN (GFA-CNN), works in a multi-task manner, performing face
anti-spoofing and face recognition simultaneously. Experimental results show
that GFA-CNN outperforms previous face anti-spoofing approaches and also well
preserves the identity information of input face images.

该论文提出了一种基于 CNN 的面部反欺骗检测方法，使用总成对混淆（TPC）损失和快速域适应（FDA）组件来提高泛化性能和适用性，同时进行面部反欺骗和面部识别的多任务学习。实验结果表明，该模型在面部反欺骗方面优于之前的方法，并很好地保留了输入面部图像的身份信息。