Realistic Dreams: Cascaded Enhancement of GAN-generated Images with an Example in Face Morphing Attacks

Abstract

The quality of images produced by generative adversarial networks (GAN) is commonly a trade-off between the model size, its training data needs, and the generation resolution. This trad-off is clear when applying GANs to issues like generating face morphing attacks, where the latent vector used by the generator is manipulated. In this paper, we propose an image enhancement solution designed to increase the quality and resolution of GAN-generated images. The solution is designed to require limited training data and be extendable to higher resolutions. We successfully apply our solution on GAN-based face morphing attacks. Beside the face recognition vulnerability and attack detectability analysis, we prove that the images enhanced by our solution are of higher visual and quantitative quality in comparison to unprocessed attacks and attack images enhanced by state-of-the-art super-resolution approaches.