SFDemorpher: Generalizable Face Demorphing for Operational Morphing Attack Detection

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.

Figures (12)

• Figure 1: Visualization of the three primary operational face demorphing scenarios. In each case, face demorphing algorithm processes a suspected document image $I_{\text{doc}}$ and a trusted reference $I_{\text{ref}}$ to estimate a ground truth $I_{\text{GT}}$. The specific restoration goal $I_{\text{out}}$ is determined by the scenario: (a) fraudulent enrollment with a morphed document, (b) border crossing with a morphed document, and (c) standard travel with a bona fide document.
• Figure 2: The training pipeline of the SFDemorpher framework utilizing a dual-pass strategy. The bona fide pass processes the input pair $(I_{\text{doc}}, I_{\text{ref}}) = (I_B, I_{B'})$ to reconstruct the ground truth $I_{\text{GT}} = I_B$, yielding output $I_{\text{out}} = I_{\hat{B}}$. The morphed pass trains on the accomplice restoration scenario using inputs $(I_{\text{doc}}, I_{\text{ref}}) = (I_{AC}, I_{C'})$ to disentangle identities, targeting the original accomplice $I_{\text{GT}} = I_A$ to reconstruct $I_{\text{out}} = I_{\hat{A}}$. During inference, the framework operates identically without the ground truth $I_{\text{GT}}$.
• Figure 3: Overview of the proposed D-MAD pipeline. The SFDemorpher framework reconstructs an output image $I_{\text{out}}$ from the document $I_{\text{doc}}$ and trusted reference $I_{\text{ref}}$. Next, an FRS computes the similarity score $s$ between the reconstruction and the reference to classify the document as either bona fide or morphed.
• Figure 4: Qualitative results of accomplice identity restoration on morphed images from the FRLL-Morphs-UTW frll_morphs_1frll_morphs_2styledemorpher dataset. Green scores (bottom right) denote the identity similarity to the ground truth accomplice, while red scores (top left) denote similarity to the non-target criminal identity in the trusted reference. Effective demorphing yields high green scores and low red scores, indicating successful target reconstruction and non-target removal.
• Figure 5: Qualitative results on the FEI Morph V2 iciap2023feimorph dataset on splicing-based splicing morphs. The visualization follows the same convention as Fig. \ref{['fig:frll_restoration_visual']}. This dataset enables evaluation of both accomplice and criminal restoration scenarios.