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

• URL: http://arxiv.org/abs/2108.10291v1
• Date: Mon, 23 Aug 2021 17:04:51 GMT
• Title: PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection
• Authors: Naser Damer, Noemie Spiller, Meiling Fang, Fadi Boutros, Florian Kirchbuchner and Arjan Kuijper
• Abstract summary: 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. Different 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.
• Score: 6.24950085812444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Different 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 during the training process, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution performs more accurately than a set of established baselines. More importantly, our approach 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 attacks and bona fide samples, namely the LMA-DRD dataset that will be made publicly available for research purposes.

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