Face Morphing Attacks and Face Image Quality: The Effect of Morphing and
the Unsupervised Attack Detection by Quality
- URL: http://arxiv.org/abs/2208.05864v2
- Date: Fri, 12 Aug 2022 07:15:35 GMT
- Title: Face Morphing Attacks and Face Image Quality: The Effect of Morphing and
the Unsupervised Attack Detection by Quality
- Authors: Biying Fu and Naser Damer
- Abstract summary: We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition.
This work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures.
Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores.
- Score: 6.889667606945215
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Morphing attacks are a form of presentation attacks that gathered increasing
attention in recent years. A morphed image can be successfully verified to
multiple identities. This operation, therefore, poses serious security issues
related to the ability of a travel or identity document to be verified to
belong to multiple persons. Previous works touched on the issue of the quality
of morphing attack images, however, with the main goal of quantitatively
proofing the realistic appearance of the produced morphing attacks. We theorize
that the morphing processes might have an effect on both, the perceptual image
quality and the image utility in face recognition (FR) when compared to bona
fide samples. Towards investigating this theory, this work provides an
extensive analysis of the effect of morphing on face image quality, including
both general image quality measures and face image utility measures. This
analysis is not limited to a single morphing technique, but rather looks at six
different morphing techniques and five different data sources using ten
different quality measures. This analysis reveals consistent separability
between the quality scores of morphing attack and bona fide samples measured by
certain quality measures. Our study goes further to build on this effect and
investigate the possibility of performing unsupervised morphing attack
detection (MAD) based on quality scores. Our study looks intointra and
inter-dataset detectability to evaluate the generalizability of such a
detection concept on different morphing techniques and bona fide sources. Our
final results point out that a set of quality measures, such as MagFace and
CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct
classification accuracy of over 70%.
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