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Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection

 
: Venkatesh, Sushma; Zhang, Haoyu; Ramachandra, Raghavendra; Raja, Kiran; Damer, Naser; Busch, Christoph

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Institute of Electrical and Electronics Engineers -IEEE-:
8th International Workshop on Biometrics and Forensics, IWBF 2020. Proceedings : April 29-30, 2020, Porto, Portugal
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6232-4
ISBN: 978-1-7281-6233-1
6 S.
International Workshop on Biometrics and Forensics (IWBF) <8, 2020, Porto>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); biometrics; face recognition; spoofing attacks; CRISP; ATHENE

Abstract
The primary objective of face morphing is to com-bine face images of different data subjects (e.g. an malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024 × 1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. (i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs? Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes.

: http://publica.fraunhofer.de/dokumente/N-590530.html