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  4. Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection
 
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2020
Conference Paper
Title

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

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.
Author(s)
Venkatesh, Sushma
NTNU
Zhang, Haoyu
NTNU
Ramachandra, Raghavendra
NTNU
Raja, Kiran
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Busch, Christoph
NTNU
Mainwork
8th International Workshop on Biometrics and Forensics, IWBF 2020. Proceedings  
Conference
International Workshop on Biometrics and Forensics (IWBF) 2020  
DOI
10.1109/IWBF49977.2020.9107970
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • 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

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