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

AutorDamer, Naser; Boutros, Fadi; Saladie, Alexandra Moseguí; Kirchbuchner, Florian; Kuijper, Arjan
ArtConference Paper
AbstraktThe 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.
KonferenzInternational Conference on Biometrics - Theory, Applications and Systems (BTAS) <10, 2019, Tampa/Fla.>
ProjektBundes­ministerium für Bildung und Forschung BMBF (Deutschland)/
ReferenzInstitute of Electrical and Electronics Engineers -IEEE-: IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019: Tampa, Florida, USA, 23 - 26 September 2019. Piscataway, NJ: IEEE, 2019, pp. 201-210