Face Morphing Attack and Detection Techniques

Face morphing attacks have emerged as a potent attack vector targeting state-of-the-art Face Recognition (FR) systems. FR, which should be tolerant with respect to intra-class variations by design, turn out to be vulnerable to such attacks. Designing algorithms to detect this emerging threat is of preeminent relevance to secure FR systems deployed across a wide range of operational applications. However, the success of developing effective Morphing Attack Detection (MAD) algorithms in a rapidly evolving landscape against synthetic (and non-synthetic in some cases) image generation technology will be highly dependent on access to the latest morph generation technology, methods, and data. By developing more openly accessible morph generation algorithms and datasets, we enable the research community to train their MAD algorithms on the most potent and effective morphing algorithms, shutting down potential attack vectors. Lastly, recent work has shown that the post-processing and the medium, i.e., printed and scanned images or purely digital images, of both the suspected image and the trusted live captured image can greatly impact the efficacy of the morphed attack. Towards this aim we invite researchers to submit papers towards this special session at IJCB 2024 under the general envelope of face morphing attack and detection techniques.