TU Darmstadt / ULB / TUbiblio

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

Damer, Naser and Boutros, Fadi and Saladie, Alexandra Mosegui and Kirchbuchner, Florian and Kuijper, Arjan (2019):
Realistic Dreams: Cascaded Enhancement of GAN-generated Images with an Example in Face Morphing Attacks.
pp. 1-10, IEEE, 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019), Tampa, USA, 23.-26.09., ISBN 978-1-7281-1523-8,
DOI: 10.1109/BTAS46853.2019.9185994,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Damer, Naser and Boutros, Fadi and Saladie, Alexandra Mosegui and Kirchbuchner, Florian and Kuijper, Arjan
Title: Realistic Dreams: Cascaded Enhancement of GAN-generated Images with an Example in Face Morphing Attacks
Language: English
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.

Publisher: IEEE
ISBN: 978-1-7281-1523-8
Uncontrolled Keywords: Biometrics, Face recognition, Image generation, Spoofing attacks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019)
Event Location: Tampa, USA
Event Dates: 23.-26.09.
Date Deposited: 22 Sep 2020 13:25
DOI: 10.1109/BTAS46853.2019.9185994
Official URL: https://doi.org/10.1109/BTAS46853.2019.9185994
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details