Abstract
The growing use of portrait images in computer vision highlights the need to protect personal identities. At the same time, anonymized images must remain useful for downstream computer vision tasks. In this work, we propose a unified framework that leverages the inpainting ability of latent diffusion models to generate realistic anonymized images. Unlike prior approaches, we have complete control over the anonymization process by designing an adaptive attribute-guidance module that applies gradient correction during the reverse denoising process, aligning the facial attributes of the generated image with those of the synthesized target image. Our framework also supports localized anonymization, allowing users to specify which facial regions are left unchanged. Extensive experiments conducted on the public CelebA-HQ and FFHQ datasets show that our method outperforms state-of-the-art approaches while requiring no additional model training. The source code is available on our page.
Abstract (translated)
随着计算机视觉中人像图片使用量的增加，保护个人身份的需求日益凸显。同时，匿名化后的图像必须仍能用于下游的计算机视觉任务。为此，我们提出了一种统一框架，利用潜在扩散模型（latent diffusion models）的修复能力来生成逼真的匿名化图像。与以往的方法不同，通过设计一种自适应属性指导模块，在逆向去噪过程中进行梯度校正，从而完全控制匿名化的全过程，并使生成的面部属性与合成的目标图像中的属性对齐。 此外，我们的框架支持局部匿名化功能，允许用户指定哪些面部区域保持不变。在公开的数据集CelebA-HQ和FFHQ上进行了广泛的实验，结果显示我们提出的方法优于当前最先进的方法，并且无需额外训练模型。源代码可在我们的页面上获取。
URL
https://arxiv.org/abs/2509.14866
