  A face morphing attack image can be verified to multiple identities, making
this attack a major vulnerability to processes based on identity verification,
such as border checks. Various methods have been proposed to detect face
morphing attacks, however, with low generalizability to unexpected
post-morphing processes. A major post-morphing process is the print and scan
operation performed in many countries when issuing a passport or identity
document. In this work, we address this generalization problem by adapting a
pixel-wise supervision approach where we train a network to classify each pixel
of the image into an attack or not, rather than only having one label for the
whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved
to perform more accurately than a set of established baselines. More
importantly, PW-MAD shows high generalizability in comparison to related works,
when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD
approach, we create a new face morphing attack dataset with digital and
re-digitized samples, namely the LMA-DRD dataset that is publicly available for
research purposes upon request.
