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• SFDemorpher achieves state-of-the-art generalizability across unseen identities, diverse capture conditions, and 13 morphing techniques.result 90%
• SFDemorpher's dual-pass training strategy, handling both morphed and bona fide documents, improves D-MAD performance by widening the margin between score distributions.method 85%
• SFDemorpher is the first face demorphing approach to use a training corpus where synthetic identities constitute the majority (80%) of the data.technical 90%
• Existing face demorphing methods lack operational generalizability due to limited training data and the assumption that all document inputs are morphs.limitation 85%
• SFDemorpher performs identity disentanglement within joint StyleGAN latent and high-dimensional feature spaces, which improves identity preservation.technical 85%
• The framework provides high-fidelity visual reconstructions that facilitate explainability for D-MAD.result 80%
• A critical barrier to real-world deployment of face demorphing is limited generalizability to unseen data distributions.limitation 90%
• The method's scenario-aware training addresses the limitation of existing methods that assume all inputs are morphs.method 85%

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