Face Recognition Systems (FRS) are vulnerable to morph attacks. A face morph is created by combining multiple identities with the intention to fool FRS and making it match the morph with multiple identities. Current Morph Attack Detection (MAD) can detect the morph but are unable to recover the identities used to create the morph with satisfactory outcomes. Existing work in de-morphing is mostly reference-based, i.e. they require the availability of one identity to recover the other. Sudipta et al. \cite{ref9} proposed a reference-free de-morphing technique but the visual realism of outputs produced were feeble. In this work, we propose SDeMorph (Stably Diffused De-morpher), a novel de-morphing method that is reference-free and recovers the identities of bona fides. Our method produces feature-rich outputs that are of significantly high quality in terms of definition and facial fidelity. Our method utilizes Denoising Diffusion Probabilistic Models (DDPM) by destroying the input morphed signal and then reconstructing it back using a branched-UNet. Experiments on ASML, FRLL-FaceMorph, FRLL-MorDIFF, and SMDD datasets support the effectiveness of the proposed method.

翻译：人脸识别系统（FRS）易受融合攻击。人脸融合图像通过组合多个身份信息生成，旨在欺骗FRS使其将该图像与多个身份匹配。当前的人脸融合攻击检测（MAD）技术虽能识别融合图像，但无法以满意效果恢复用于生成该图像的身份信息。现有去融合研究大多基于参考图像方法，即需获取其中一个身份信息方可恢复另一个。Sudipta等人\cite{ref9}提出了一种无参考去融合技术，但其输出结果的视觉真实性较差。本研究提出SDeMorph（基于稳定扩散模型的去融合器），这是一种新颖的无参考去融合方法，可恢复真实身份信息。该方法能生成定义清晰、面部保真度极高的高质量特征丰富输出结果。其核心技术利用去噪扩散概率模型（DDPM），通过破坏输入融合信号并利用分支UNet网络进行重建。在ASML、FRLL-FaceMorph、FRLL-MorDIFF及SMDD数据集上的实验验证了所提方法的有效性。