A facial morph is an image created by combining two face images pertaining to two distinct identities. Face demorphing inverts the process and tries to recover the original images constituting a facial morph. While morph attack detection (MAD) techniques can be used to flag morph images, they do not divulge any visual information about the faces used to create them. Demorphing helps address this problem. Existing demorphing techniques are either very restrictive (assume identities during testing) or produce feeble outputs (both outputs look very similar). In this paper, we overcome these issues by proposing dc-GAN, a novel GAN-based demorphing method conditioned on the morph images. Our method overcomes morph-replication and produces high quality reconstructions of the bonafide images used to create the morphs. Moreover, our method is highly generalizable across demorphing paradigms (differential/reference-free). We conduct experiments on AMSL, FRLL-Morphs and MorDiff datasets to showcase the efficacy of our method.

翻译：人脸融合图像是通过将属于两个不同身份的人脸图像组合而创建的图像。人脸解构旨在逆转此过程，尝试从一张融合图像中恢复构成它的原始图像。虽然融合攻击检测技术可用于标记融合图像，但它们无法揭示用于创建这些融合图像的原始人脸的任何视觉信息。解构技术有助于解决这一问题。现有的解构技术要么限制性很强（在测试阶段需假设身份信息），要么生成效果不佳（两个输出结果看起来非常相似）。在本文中，我们通过提出dc-GAN克服了这些问题，这是一种新颖的基于生成对抗网络的解构方法，其生成过程以融合图像为条件。我们的方法克服了融合复制问题，并高质量地重建了用于创建融合图像的真实原始图像。此外，我们的方法在解构范式（差分/无参考）上具有高度的泛化能力。我们在AMSL、FRLL-Morphs和MorDiff数据集上进行了实验，以展示我们方法的有效性。