面部去变形：基于身份保持的图像分解
Facial Demorphing via Identity Preserving Image Decomposition
AI总结：
针对面部变形攻击，提出一种无需参考的身份保持图像分解方法，通过分解与合并网络高精度恢复真实身份图像，并在多个数据集上验证了有效性。
摘要：
面部变形图像是通过组合通常属于两个不同身份的面部图像而创建的。其目标是生成一张能够与两个身份匹配的图像，从而破坏人脸识别系统的安全性。为了解决这一问题，人们已经开发了多种变形攻击检测技术。但这些方法并不能提取出用于创建变形图像的底层真实身份图像的任何信息。去变形技术解决了这一局限性。然而，当前的去变形技术大多是基于参考的，即它们需要其中一个身份的图像来恢复另一个身份。在这项工作中，我们将去变形视为一个不适定的分解问题。我们提出了一种新颖的、无需参考的方法，能够高精度地恢复真实身份图像。我们的方法将变形图像分解为多个保持身份的特征组件。然后，一个合并网络对这些组件进行加权和组合，以恢复真实身份图像。我们观察到，该方法在清晰度和保真度方面能够重建高质量的真实身份图像。在CASIA-WebFace、SMDD和AMSL数据集上的实验证明了我们方法的有效性。
英文摘要：
A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
