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

AutorVenkatesh, Sushma; Zhang, Haoyu; Ramachandra, Raghavendra; Raja, Kiran; Damer, Naser; Busch, Christoph
ArtConference Paper
AbstraktThe 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.
KonferenzInternational Workshop on Biometrics and Forensics (IWBF) <8, 2020, Porto>
ReferenzInstitute 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, 6 pp.
SchlüsselISBN : 9781728162324