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The primary objective of face morphing is to combine face images of differentdata subjects (e.g. a malicious actor and an accomplice) to generate a faceimage that can be equally verified for both contributing data subjects. In thispaper, we propose a new framework for generating face morphs using a newerGenerative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works,we generate realistic morphs of both high-quality and high resolution of1024$\times$1024 pixels. With the newly created morphing dataset of 2500morphed face images, we pose a critical question in this work. \textit{(i) CanGAN generated morphs threaten Face Recognition Systems (FRS) equally asLandmark based morphs?} Seeking an answer, we benchmark the vulnerability of aCommercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace).This work also benchmarks the detection approaches for both GAN generatedmorphs against the landmark based morphs using established Morphing AttackDetection (MAD) schemes.

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