IDSwapMAD: towards privacy-friendly training of differential face morphing attack detection

Abstract Privacy regulations and ethical concerns have encouraged the use of privacy-friendly synthetic data for the training of facial analysis systems. However, the automated generation of images depicting the same synthetic subject in different environmental scenarios remains challenging, as identity-related features may not be accurately preserved. This is a severe issue for the training of differential morphing attack detection (MAD) algorithms, where subtle differences in facial features can indicate morphing attacks. This work introduces IDSwapMAD as a new way for generating privacy-friendly training data for differential MAD methods. In detail, a generative adversarial network is employed to generate synthetic facial images of which the faces are swapped with pairs of real reference and probe images containing variations that mimic a border control scenario. In this way, style-related properties of the reference and probe images are retained, while identity-related features are replaced. It is shown that the proposed IDSwapMAD technique is an effective and privacy-friendly strategy for training differential MAD methods, whose detection performance is on par with a state-of-the-art MAD method trained on real data.