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

Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection