PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection
This addresses a major vulnerability in identity verification systems like border checks, offering improved detection for face morphing attacks with better generalization to real-world scenarios, though it is an incremental improvement over existing methods.
The paper tackles the problem of low generalizability in face morphing attack detection, especially for post-morphing processes like print and scan, by proposing a pixel-wise supervision approach that classifies each pixel as attack or not, resulting in more accurate performance and high generalizability on unknown re-digitized attacks compared to established baselines.
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.