Face morphing attacks and face image quality: The effect of morphing and the unsupervised attack detection by quality

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 studies 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. The authors theorise 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. The authors’ study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. The authors’ study looks into intra‐ and inter‐dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The authors’ final results point out that a set of quality measures, such as MagFace and CNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.

MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders

“…Other work showed that the visibly unrealistic images of MorGAN have higher statistical quality metrics (6 different metrics [7]) than other attacks. In a recent study, researchers have shown that operations that apparent morphing artifacts do not consistently affect the estimated quality across a large number of quality estimation strategies [17], [18], however, especially utility metrics, can be used to differentiate between attacks and bona fide to a small degree [17]. However, studies on the human observer's ability to detect the MorDIFF attack are necessary and are planned to evaluate the relative perceived quality of these attacks, as conducted on previous attack methods in [19].…”