PW-MAD: Pixel-Wise Supervision for Generalized Face Morphing Attack Detection

AuthorDamer, Naser; Spiller, Noémie Catherine Hélène; Fang, Meiling; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, Arjan
TypeConference Paper
AbstractA 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.
ConferenceInternational Symposium on Visual Computing (ISVC) <16, 2021, Online>
ProjectBundes­ministerium für Bildung und Forschung BMBF (Deutschland)/
PartBebis, G.: Advances in Visual Computing. 16th International Symposium, ISVC 2021. Proceedings. Pt.I: Virtual Event, October 4-6, 2021. Cham: Springer Nature, 2021. (Lecture Notes in Computer Science 13017), pp. 291-304
PartnISBN : 9783030904388