SFDemorpher: Generalizable Face Demorphing for Operational Morphing Attack Detection

TL;DR

SFDemorpher is a novel face demorphing framework that enhances operational morphing attack detection by disentangling identities in joint latent and feature spaces, demonstrating high generalizability and explainability.

Contribution

It introduces a dual-pass training strategy with synthetic data to improve face demorphing robustness across unseen identities and morphing techniques.

Findings

Achieves state-of-the-art generalizability across unseen identities and morphing methods.

Widened score distribution margin improves morphing attack detection accuracy.

Provides high-fidelity visual reconstructions for better explainability.

Abstract

Face morphing attacks compromise biometric security by creating document images that verify against multiple identities, posing significant risks from document issuance to border control. Differential Morphing Attack Detection (D-MAD) offers an effective countermeasure, particularly when employing face demorphing to disentangle identities blended in the morph. However, existing methods lack operational generalizability due to limited training data and the assumption that all document inputs are morphs. This paper presents SFDemorpher, a framework designed for the operational deployment of face demorphing for D-MAD that performs identity disentanglement within joint StyleGAN latent and high-dimensional feature spaces. We introduce a dual-pass training strategy handling both morphed and bona fide documents, leveraging a hybrid corpus with predominantly synthetic identities to enhance robustness against unseen distributions. Extensive evaluation confirms state-of-the-art generalizability across unseen identities, diverse capture conditions, and 13 morphing techniques, spanning both border verification and the challenging document enrollment stage. Our framework achieves superior D-MAD performance by widening the margin between the score distributions of bona fide and morphed samples while providing high-fidelity visual reconstructions facilitating explainability.