Abstract
The increasing prevalence of computer vision applications necessitates handling vast amounts of visual data, often containing personal information. While this technology offers significant benefits, it should not compromise privacy. Data privacy regulations emphasize the need for individual consent for processing personal data, hindering researchers' ability to collect high-quality datasets containing the faces of the individuals. This paper presents a deep learning-based face anonymization pipeline to overcome this challenge. Unlike most of the existing methods, our method leverages recent advancements in diffusion-based inpainting models, eliminating the need for training Generative Adversarial Networks. The pipeline employs a three-stage approach: face detection with RetinaNet, feature extraction with VGG-Face, and realistic face generation using the state-of-the-art BrushNet diffusion model. BrushNet utilizes the entire image, face masks, and text prompts specifying desired facial attributes like age, ethnicity, gender, and expression. This enables the generation of natural-looking images with unrecognizable individuals, facilitating the creation of privacy-compliant datasets for computer vision research.
Abstract (translated)
计算机视觉应用程序的日益普及需要处理大量包含个人信息的视觉数据。虽然这项技术提供了许多好处，但不应以牺牲隐私为代价。数据隐私法规强调了在处理个人信息时获取个体同意的重要性，这限制了研究人员收集高质量的人脸数据集的能力。本文提出了一种基于深度学习的脸部匿名化管道，旨在克服这一挑战。 与大多数现有方法不同，我们的方法利用了基于扩散的图像修复模型的最新进展，从而无需训练生成对抗网络（GAN）。该管道采用三阶段方法：使用RetinaNet进行脸部检测、用VGG-Face提取特征以及利用最先进的BrushNet扩散模型生成逼真的面部图像。BrushNet利用整个图像、脸部掩码和文本提示（包括所需的面部属性如年龄、种族、性别和表情）来生成自然且无法识别个人身份的图像，从而促进计算机视觉研究中符合隐私合规的数据集的创建。
URL
https://arxiv.org/abs/2505.21002
