Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face

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 proposed deep learning-based approach achieves a morphing attack potential comparable to that of landmark-based techniques, which have traditionally been regarded as the most challenging. These findings confirm the ability of the proposed method to effectively preserve and manage identity information during the morph generation process.