Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for detecting fake login sessions without specialized sensors. Current CNN-based method perform well on the domains they were trained for, but often show poor generalization on previously unseen datasets. In this paper we describe a method for utilizing unsupervised pretraining for improving performance across multiple datasets without any adaptation, introduce the Entry Antispoofing Dataset for supervised fine-tuning, and propose a multi-class auxiliary classification layer for augmenting the binary classification task of detecting spoofing attempts with explicit interpretable signals. We demonstrate the efficiency of our model by achieving state-of-the-art results on cross-dataset testing on MSU-MFSD, Replay-Attack, and OULU-NPU datasets.

翻译：由于生物鉴别认证系统的安全要求很高,脸部防污系统引起了许多注意。将脸部生物鉴别技术带到商业硬件中,主要取决于开发可靠的方法,在没有专门传感器的情况下探测假登录过程。目前以CNN为基础的方法在它们所培训的领域表现良好,但往往对先前看不见的数据集没有很好地加以概括化。在本文中,我们描述了一种方法,即利用未经监督的预先培训来改进多个数据集的性能,而不作任何调整,引入了用于监督微调的条目防伪数据集,并提议了一个多级辅助分类层,以扩大用明确的可解释信号探测伪图的二元分类任务。我们通过在MSU-MFSD、Replay-Atack和OULU-NPU数据集的交叉数据测试中取得最先进的结果,展示了我们模型的效率。