Face recognition systems are widely deployed in high-security applications such as for biometric verification at border controls. Despite their high accuracy on pristine data, it is well-known that digital manipulations, such as face morphing, pose a security threat to face recognition systems. Malicious actors can exploit the facilities offered by the identity document issuance process to obtain identity documents containing morphed images. Thus, subjects who contributed to the creation of the morphed image can with high probability use the identity document to bypass automated face recognition systems. In recent years, no-reference (i.e., single image) and differential morphing attack detectors have been proposed to tackle this risk. These systems are typically evaluated in isolation from the face recognition system that they have to operate jointly with and do not consider the face recognition process. Contrary to most existing works, we present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks. To this end, we introduce TetraLoss, a novel loss function that learns to separate morphed face images from its contributing subjects in the embedding space while still achieving high biometric verification performance. In a comprehensive evaluation, we show that the proposed method can significantly enhance the original system while also significantly outperforming other tested baseline methods.

翻译：人脸识别系统广泛应用于高安全性场景，例如边境管控中的生物特征验证。尽管此类系统在原始数据上具有很高的准确性，但众所周知，数字篡改（如人脸融合）对人脸识别系统构成了安全威胁。恶意行为者可能利用身份证件签发流程提供的便利，获取包含融合图像的身份证件。因此，参与生成融合图像的个体极有可能使用该身份证件绕过自动化人脸识别系统。近年来，学界已提出无参考（即单图像）和差分融合攻击检测器来应对此风险。这些系统通常独立于其需协同运行的人脸识别系统进行评估，且未考虑人脸识别过程本身。与大多数现有研究不同，本文提出一种新颖方法，使基于深度学习的人脸识别系统能够更好地抵御人脸融合攻击。为此，我们引入TetraLoss——一种新型损失函数，该函数能够在嵌入空间中学习将融合人脸图像与其贡献主体分离，同时仍保持优异的生物特征验证性能。通过全面评估，我们证明所提方法能显著增强原始系统性能，并明显优于其他测试基准方法。