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MorGAN: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Generative Adversarial Network

 
: Damer, Naser; Moseguí Saladié, Alexandra; Braun, Andreas; Kuijper, Arjan

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Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 : Redondo Beach, California, USA 22-25 October 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7180-1
ISBN: 978-1-5386-7179-5
ISBN: 978-1-5386-7181-8
10 S.
International Conference on Biometrics - Theory, Applications and Systems (BTAS) <9, 2018, Redondo Beach/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Smart City; Visual Computing as a Service; Computer vision (CV); Human computer interaction (HCI); Biometrics; Face recognition; Spoofing attacks; Deep learning; CRISP

Abstract
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border crossing. Research has been focused on creating more accurate attack detection approaches by considering different image properties. However, all the attacks considered so far are based on manipulating facial landmarks localized in the morphed face images. In contrast, this work presents novel face morphing attacks based on image generated by generative adversarial networks. We present the MorGAN structure that considers the representation loss to successfully create realistic morphing attacks. Based on that, we present a novel face morphing attacks database (MorGAN database) that contains 1000 morph images for both, the proposed MorGAN and landmark-based attacks. We present vulnerability analysis of two face recognition approaches facing the proposed attacks. Moreover, the detectability of the proposed MorGAN attacks is studied, in the scenarios where this type of attacks is know and unknown. We concluded with pointing out the challenge of detecting such unknown novel attacks and an analysis of detection performances of different features in detecting such attacks.

: http://publica.fraunhofer.de/dokumente/N-546275.html