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To address this, we propose a novel face demorphing framework that leverages the latent space of StyleGAN2. At its core is ReStyle-ID, an encoder network optimized for identity preservation through improved loss functions and targeted training data, enabling accurate and identity-focused inversion. Combined with StyleDemorpher, a face demorphing network trained on a novel DemorphDB dataset with high-quality morph images that simulate realistic and challenging attack scenarios, the framework reconstructs high-resolution demorphed faces and generalizes well to unseen identities and morphing methods. Together, these components overcome key limitations of prior approaches, such as low resolution, poor robustness, and visual artifacts. 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