Gong Maoguo, Liu Jialu, Li Hao, Xie Yu, Tang Zedong
IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):244-256. doi: 10.1109/TNNLS.2020.3027617. Epub 2022 Jan 5.
Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most of the previous methods for face deidentification rely on attribute supervision to preserve a certain kind of identity-independent utility but lose the other identity-independent data utilities. In this article, we mainly propose a novel disentangled representation learning architecture for multiple attributes preserving face deidentification called replacing and restoring variational autoencoders (RVAEs). The RVAEs disentangle the identity-related factors and the identity-independent factors so that the identity-related information can be obfuscated, while they do not change the identity-independent attribute information. Moreover, to improve the details of the facial region and make the deidentified face blends into the image scene seamlessly, the image inpainting network is employed to fill in the original facial region by using the deidentified face as a priori. Experimental results demonstrate that the proposed method effectively deidentifies face while maximizing the preservation of the identity-independent information, which ensures the semantic integrity and visual quality of shared images.
面部是视觉共享数据中最具吸引力的敏感信息之一。在共享数据时，设计一种有效的面部去识别方法以在面部隐私保护和数据实用性之间取得平衡是一项紧迫的任务。以前大多数面部去识别方法依赖于属性监督来保留某种与身份无关的实用性，但却丢失了其他与身份无关的数据实用性。在本文中，我们主要提出了一种用于多属性保留面部去识别的新颖解缠表示学习架构，称为替换和恢复变分自编码器（RVAEs）。RVAEs将与身份相关的因素和与身份无关的因素解缠，以便可以混淆与身份相关的信息，同时它们不会改变与身份无关的属性信息。此外，为了改善面部区域的细节并使去识别后的面部无缝融入图像场景，采用图像修复网络以去识别后的面部为先验来填充原始面部区域。实验结果表明，所提出的方法在有效对面部进行去识别的同时，最大程度地保留了与身份无关的信息，从而确保了共享图像的语义完整性和视觉质量。
