MorphUNet: Alpha-Controlled Biometric Transport for Diffusion-Based Face Morphing Attacks
MorphUNet is a novel diffusion-based morphing framework that formulates two-parent generation as alpha-controlled biometric transport, using CLIP appearance and ArcFace identity evidence with trainable dual cross-attention. It achieves state-of-the-art Morphing Attack Potential (MAP) on FEI and FRLL datasets, and remains highly difficult to detect under cross-dataset transfer. The framework includes stress testing with unseen identities from CFD.
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[Submitted on 27 Jul 2026]
Title:MorphUNet: Alpha-Controlled Biometric Transport for Diffusion-Based Face Morphing Attacks
View a PDF of the paper titled MorphUNet: Alpha-Controlled Biometric Transport for Diffusion-Based Face Morphing Attacks, by Taimoor Rizwan and 4 other authors
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Abstract:Face morphing attacks create synthetic images verifiable against multiple identities, threatening border control and identity verification systems. We introduce MorphUNet, a diffusion morphing framework formulating two-parent generation as alpha-controlled biometric transport: each parent is decomposed into CLIP appearance and ArcFace identity evidence, aligned into a CLIP-compatible token space, with the two contributors preserved as separate identity-aware token banks. To our knowledge, MorphUNet is the first diffusion-based morphing framework using trainable parent-separated dual cross-attention inside the denoising U-Net: a Biometric Transport Layer carrying parent-specific identity evidence through denoising, attending to each parent separately before combining residuals via the morphing parameter alpha. DDIM-inverted latent interpolation gives a coherent denoising start, while weaker-parent-guided selection favours morphs maximising the lower parent-similarity score, reducing collapse toward one contributor. We evaluate MorphUNet against three state-of-the-art baselines (StableMorph, MIPGAN-II, and MorDIFF) on FEI and FRLL using six recognition systems, and propose CFD-based unseen-identity stress testing across gender and ethnicity pairing, demographic shifts, and parent-similarity extremes. MorphUNet achieves the best Morphing Attack Potential (MAP) when at least three of six systems are fooled by one morph, reaching 0.919 on FEI and 0.886 on FRLL, and obtains the best FID on both datasets (35.19 FEI, 44.86 FRLL). It also gives the highest APCER at 5% BPCER in the same-dataset setting, and remains highly difficult to detect under cross-dataset transfer, with APCER 0.996 on FEI and 0.946 on FRLL. The full evaluation analyses MAP, MAD, per-system vulnerability, identity balance, image quality, top/bottom-similarity stress tests, and CFD unseen-identity robustness.
Comments: 37 pages, 23 figures, 8 tables. Includes supplementary material (additional robustness analysis, CFD stress tests, and conditioning ablations) appended after the references
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.25092 [cs.CV]
(or arXiv:2607.25092v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.25092
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Taimoor Rizwan [view email] [v1] Mon, 27 Jul 2026 21:30:49 UTC (16,211 KB)
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