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

• URL: http://arxiv.org/abs/2007.03621v1
• Date: Tue, 7 Jul 2020 16:52:56 GMT
• Title: Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection
• Authors: Sushma Venkatesh, Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja, Naser Damer, Christoph Busch
• Abstract summary: We propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work.
• Score: 22.220940043294334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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