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

Venkatesh, Sushma ; Zhang, Haoyu ; Ramachandra, Raghavendra ; Raja, Kiran ; Damer, Naser ; Busch, Christoph (2020):
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection.
In: 2020 8th International Workshop on Biometrics and Forensics (IWBF), pp. 1-6,
Los Alamitos, Calif., Porto, Portugal, 29-30 April 2020, DOI: 10.1109/IWBF49977.2020.9107970,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Venkatesh, Sushma ; Zhang, Haoyu ; Ramachandra, Raghavendra ; Raja, Kiran ; Damer, Naser ; Busch, Christoph
Title: Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection
Language: English
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.

Book Title: 2020 8th International Workshop on Biometrics and Forensics (IWBF)
Place of Publication: Los Alamitos, Calif.
Uncontrolled Keywords: Biometrics, Face recognition, Spoofing attacks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Location: Porto, Portugal
Event Dates: 29-30 April 2020
Date Deposited: 08 Jun 2020 10:20
DOI: 10.1109/IWBF49977.2020.9107970
URL / URN: https://doi.org/10.1109/IWBF49977.2020.9107970
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