Venkatesh, SushmaSushmaVenkateshZhang, HaoyuHaoyuZhangRamachandra, RaghavendraRaghavendraRamachandraRaja, KiranKiranRajaDamer, NaserNaserDamerBusch, ChristophChristophBusch2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/40816810.1109/IWBF49977.2020.9107970The 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.enLead Topic: Smart CityLead Topic: Visual Computing as a ServiceResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)biometricsface recognitionspoofing attacksCRISPATHENE006conference paper