Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphing Attacks. We build upon previous embedding studies in both use cases using the MagFace model. For the first objective, we employ an pre-selection algorithm that pairs individuals based on face embedding similarity. We quantify the attack potential of differently morphed face images to compare the usability of pre-selection in automatically generating numerous successful Morphing Attacks. Regarding the second objective, we compare embeddings from two state-of-the-art face recognition systems in terms of their ability to detect Morphing Attacks. Our findings demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Both open-source and COTS face recognition systems are susceptible to generated attacks, particularly when pre-selection is based on embeddings rather than random pairing which was only constrained by soft biometrics. More accurate face recognition systems exhibit greater vulnerability to attacks, with COTS systems being the most susceptible. Additionally, MagFace embeddings serve as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the advantages of face embeddings in more effective image pre-selection for face morphing and accurate detection of morphed face images. This is supported by extensive analysis of various designed attacks. The MagFace model proves to be a powerful alternative to the commonly used ArcFace model for both objectives, pre-selection and attack detection.

翻译：人脸变形攻击对身份证件的安全性构成威胁，尤其是在后续访问控制过程中，因为它使得涉及的双方都能利用同一证件。本研究中，面部嵌入服务于两个目的：为大规模变形攻击生成预选图像，以及检测潜在的变形攻击。我们在两种使用场景中基于MagFace模型扩展了先前的嵌入研究。针对第一个目标，我们采用了一种基于面部嵌入相似度进行个体配对的预选算法。通过量化不同变形人脸图像的攻击潜力，我们比较了预选在自动生成大量成功变形攻击中的可用性。针对第二个目标，我们比较了两种最先进人脸识别系统的嵌入在检测变形攻击方面的能力。研究结果表明，ArcFace和MagFace为图像预选提供了有价值的面部嵌入。开源和商业现成（COTS）人脸识别系统均易受生成的攻击影响，特别是当基于嵌入而非仅受软生物特征约束的随机配对进行预选时。更准确的人脸识别系统表现出更高的攻击脆弱性，其中COTS系统最为敏感。此外，与之前使用的ArcFace嵌入相比，MagFace嵌入可作为检测变形人脸图像的稳健替代方案。结果证实了面部嵌入在更有效的人脸变形图像预选及准确检测变形人脸图像方面的优势，这得到了对各种设计攻击的广泛分析的支持。在预选和攻击检测这两个目标上，MagFace模型均被证明是常用ArcFace模型的有力替代方案。