Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face

2026-02-18

AI Summary

This work proposes a novel face morphing attack method leveraging the Arc2Face identity-conditioned generative model to address the vulnerability of unsupervised facial image acquisition in electronic identity documents. By exploiting compact identity representations, the approach efficiently synthesizes highly realistic morphed faces that strongly preserve source identity information while maintaining high attack potency. As the first study to apply identity-conditioned foundation models to morphing attacks, it demonstrates on benchmark datasets such as FEI and ONOT that the proposed method achieves attack performance comparable to the current state-of-the-art landmark-based techniques, thereby validating both the effectiveness and the security threat posed by identity-driven morphing strategies.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• face morphing attacks
• face recognition
• identity documents
• morphing attack detection
• identity preservation

Innovation

• Arc2Face
• identity-preserving morphing
• face morphing attack
• foundation model
• morphing attack potential