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  4. Realistic Dreams: Cascaded Enhancement of GAN-generated Imageswith an Example in Face Morphing Attacks
 
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2019
  • Konferenzbeitrag

Titel

Realistic Dreams: Cascaded Enhancement of GAN-generated Imageswith 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.
Author(s)
Damer, Naser
Fraunhofer-Institut fĂĽr Graphische Datenverarbeitung IGD
Boutros, Fadi
Fraunhofer-Institut fĂĽr Graphische Datenverarbeitung IGD
Saladie, Alexandra MoseguĂ­
Fraunhofer-Institut fĂĽr Graphische Datenverarbeitung IGD
Kirchbuchner, Florian
Fraunhofer-Institut fĂĽr Graphische Datenverarbeitung IGD
Kuijper, Arjan
Fraunhofer-Institut fĂĽr Graphische Datenverarbeitung IGD
Hauptwerk
IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019
Project(s)
CRISP
Funder
Bundesministerium fĂĽr Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Biometrics - Theory, Applications and Systems (BTAS) 2019
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DOI
10.1109/BTAS46853.2019.9185994
Language
Englisch
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IGD
Tags
  • Lead Topic- Smart Cit...

  • Lead Topic- Visual Co...

  • Research Line- Comput...

  • Research Line- Human ...

  • Biometrics

  • Face recognition

  • Image generation

  • Spoofing attacks

  • CRISP

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