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 [BibTeX] [Marc21]
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Type of publication: Idiap-RR
Citation: Sarkar_Idiap-RR-38-2020
Number: Idiap-RR-38-2020
Year: 2020
Month: 12
Institution: Idiap
Address: 19 Rue Macroni, 1920 Martigny
Abstract: Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (Style-GAN), generated with original face images from three public face datasets. We also conduct extensive experiments to assess the vulnerability of the state-of-the-art face recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments demonstrate that VGG-Face, while being less accurate face recognition system compared to FaceNet, is also less vulnerable to morphing attacks. Also, we observed that naı̈ve morphs generated with a StyleGAN do not pose a significant threat.
Keywords: Biometrics, Face Recognition, Morphing Attack, StyleGAN 2, Vulnerability Analysis
Projects Idiap
Authors Sarkar, Eklavya
Korshunov, Pavel
Colbois, Laurent
Marcel, Sébastien
Added by: [ADM]
Total mark: 0
  • Sarkar_Idiap-RR-38-2020.pdf (MD5: 3afa1747f2af37f6e7cb37c8516d17e5)