Abstract
Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.
Highlights
M ORPHING attacks pose threats to Face Recognition Systems (FRS) by exploiting the tolerance towards intra-subject variations
The results observed in the Digital Image Benchmark (DMAD-SOTAMD_D-1.0) are reported in Figure 7 ( Table X in Appendix, as shown in the Supplementary Material, for the results on two subsets with morphing factor 0.3 and 0.5 respectively)
The good performance of DFR can be attributed to the fact that the ArcFace algorithm used for feature extraction was trained independently of morphed images and the extracted feature vectors are not overfitted to the artifacts of individual Morphing Attack Detection (MAD) algorithms
Summary
M ORPHING attacks pose threats to Face Recognition Systems (FRS) by exploiting the tolerance towards intra-subject variations. When a malicious actor is granted a valid identity document, he/she can use it for various purposes posing a risk to national security in the worst possible scenarios. With such an assertion, the initial work demonstrating the morphing attacks illustrated that commercial-off-the-shelf (COTS) FRS could be defeated with a given set of morphed images [1]. That study further assessed if morphing attacks would succeed when presented to border guards This means morphing attacks pose a threat to FRS systems and leave a major security risk to any nation where the malicious actor enters. In pursuit of the current State Of The Art (SOTA) in MAD, we first review the related work
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