Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face
Nicol\`o Di Domenico, Annalisa Franco, Matteo Ferrara, Davide Maltoni

TL;DR
This paper introduces Arc2Morph, a novel face morphing technique using Arc2Face that effectively preserves identity, posing significant challenges to face recognition systems and highlighting new vulnerabilities in passport verification processes.
Contribution
The paper presents a new deep learning-based face morphing method utilizing Arc2Face, demonstrating comparable attack potential to landmark-based techniques and revealing vulnerabilities in identity verification.
Findings
Achieves morphing attack potential similar to landmark-based methods
Effectively preserves identity information during morph generation
Performs well on large-scale and novel face datasets
Abstract
Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critical vulnerability in passport enrollment procedures adopted by many countries, where the facial image is often acquired without a supervised live capture process. In this paper, we propose a novel face morphing technique based on Arc2Face, an identity-conditioned face foundation model capable of synthesizing photorealistic facial images from compact identity representations. We demonstrate the effectiveness of the proposed approach by comparing the morphing attack potential metric on two large-scale sequestered face morphing attack detection datasets against several state-of-the-art morphing methods, as well as on two novel morphed face datasets derived from FEI and ONOT. Experimental results show that the…
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Taxonomy
TopicsFace recognition and analysis · Biometric Identification and Security · Face and Expression Recognition
