Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.

翻译：生物特征系统容易受到使用各种展示攻击工具（PAI）实施的展示攻击（PA）的影响。尽管基于深度学习和手工特征存在众多展示攻击检测（PAD）技术，但对于未知PAI的PAD泛化仍然是一个具有挑战性的问题。在本工作中，我们通过实验证明，PAD模型的初始化是泛化能力的关键因素，而这一点在学术界鲜有讨论。基于这一观察，我们提出了一种基于自监督学习的方法，称为DF-DM。具体而言，DF-DM基于全局-局部视图结合去折叠（De-Folding）与去混合（De-Mixing）来推导PAD的任务特定表示。在去折叠过程中，所提出的技术将通过显式最小化生成损失来学习区域特定特征，以局部模式表示样本。而去混合则通过最小化基于插值的一致性，驱动检测器获取具有全局信息的实例特定特征，以实现更全面的表示。大量实验结果表明，与最先进方法相比，所提出方法在更复杂和混合数据集上的面部和指纹PAD中均能实现显著提升。当在CASIA-FASD和Idiap Replay-Attack数据集上训练时，该方法在OULU-NPU和MSU-MFSD数据集上可实现18.60%的等错误率（EER），比基线性能提升9.54%。所提出技术的源代码可在https://github.com/kongzhecn/dfdm获取。