Publications
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection
| Author | Venkatesh, Sushma; Zhang, Haoyu; Ramachandra, Raghavendra; Raja, Kiran; Damer, Naser; Busch, Christoph |
|---|---|
| Date | 2020 |
| Type | Conference Paper |
| 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. |
| Conference | International Workshop on Biometrics and Forensics (IWBF) 2020 |
| Url | https://publica.fraunhofer.de/handle/publica/408168 |
