Biometric systems are vulnerable to the 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. The common problem with existing deep learning-based PAD techniques is that they may struggle with local optima, resulting in weak generalization against different PAs. In this work, we propose to use self-supervised learning to find a reasonable initialization against local trap, so as to improve the generalization ability in detecting PAs on the biometric system.The proposed method, denoted as IF-OM, 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 maximizing cycle consistency. While, De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by maximizing topological 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 the state-of-the-art methods. Specifically, when training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. Code will be made publicly available.

翻译：尽管存在基于深层次学习和手工制作特征的多种演示攻击探测技术,但推广PAD用于未知PAI仍是一个具有挑战性的问题。现有深层次学习的PAD技术的共同问题是,它们可能与当地选择发生困难,导致对不同PA的概括化不力。在这项工作中,我们提议使用自我监督的学习方法,找到合理初始化的地方陷阱,以便提高在生物鉴别系统中检测PA的60个通用能力。 拟议的方法,称为IF-OM,是以全球本地观点为基础,同时使用不易变和不混合的方法,以得出PADD。During De-Folding,拟议技术将学习特定区域特征,以当地模式代表样本,明确最大限度地实现周期一致性。虽然通过解分解驱动驱动探测器,获取全球信息的具体特征,以便更全面地展示生物鉴别系统。在使用纸面上的准确度一致性时,将降低IFO-RFA的精确度提高成本,并在使用拟议的方法中实现标准化的精确度,在使用精确度培训时,将数据与精确度实现。