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

TL;DR

This paper introduces PW-MAD, a pixel-wise supervised method for face morphing attack detection that improves accuracy and generalizability, especially on re-digitized attacks, and provides a new dataset for research.

Contribution

The paper proposes a novel pixel-wise supervision approach for face morphing attack detection, enhancing generalization to post-morphing processes and introducing a new dataset.

Findings

PW-MAD outperforms established baselines in accuracy.

PW-MAD demonstrates high generalizability to re-digitized attacks.

A new face morphing attack dataset (LMA-DRD) is created and made publicly available.

Abstract

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.