  The primary objective of face morphing is to combine face images of different
data subjects (e.g. a 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$\times$1024 pixels. With the newly created morphing dataset of 2500
morphed face images, we pose a critical question in this work. \textit{(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.
