  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.
