  Face morphing attacks aim at creating face images that are verifiable to be
the face of multiple identities, which can lead to building faulty identity
links in operations like border checks. While creating a morphed face detector
(MFD), training on all possible attack types is essential to achieve good
detection performance. Therefore, investigating new methods of creating
morphing attacks drives the generalizability of MADs. Creating morphing attacks
was performed on the image level, by landmark interpolation, or on the
latent-space level, by manipulating latent vectors in a generative adversarial
network. The earlier results in varying blending artifacts and the latter
results in synthetic-like striping artifacts. This work presents the novel
morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using
a GAN-based generation, as well as, eliminate the manipulation in the latent
space, resulting in visibly realistic morphed images compared to previous
works. The generated ReGenMorph appearance is compared to recent morphing
approaches and evaluated for face recognition vulnerability and attack
detectability, whether as known or unknown attacks.
