用文本生成模型实现高保真全身匿名，保护隐私同时保留关键特征。
Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models
- 基于文本到图像扩散模型，生成逼真匿名全身图像。
- 在图像质量、分辨率及评估指标上优于现有方法。
- 兼容最新生成模型，适合隐私保护数据集构建。
匿名化在保护真实数据集中个人敏感信息方面至关重要。例如自动驾驶汽车需要高分辨率面部特征来追踪人员及其视线方向，以预测未来行为并作出相应反应。为在保护隐私的同时保留数据集中的重要信息，有必要将人物的全身替换为高度详细的匿名形象。与仅匿名面部不同，全身替换能有效降低通过发型或服装识别个体的可能性。本文提出一种利用Stable Diffusion作为生成后端的全身匿名化工作流程。文本到图像扩散模型（如Stable Diffusion、OpenAI的DALL-E或Midjourney）近年来广受欢迎，能够仅凭单一文本提示生成逼真图像。实验表明，该方法在图像质量、分辨率、Inception Score（IS）和Frechet Inception Distance（FID）等方面均优于当前最优匿名化方案。此外，该方法对图像生成器具有不变性，可适配最新可用模型。
原文摘要 · Abstract (English)
Anonymization plays a key role in protecting sensible information of individuals in real world datasets. Self-driving cars for example need high resolution facial features to track people and their viewing direction to predict future behaviour and react accordingly. In order to protect people's privacy whilst keeping important features in the dataset, it is important to replace the full body of a person with a highly detailed anonymized one. In contrast to doing face anonymization, full body replacement decreases the ability of recognizing people by their hairstyle or clothes. In this paper, we propose a workflow for full body person anonymization utilizing Stable Diffusion as a generative backend. Text-to-image diffusion models, like Stable Diffusion, OpenAI's DALL-E or Midjourney, have become very popular in recent time, being able to create photorealistic images from a single text prompt. We show that our method outperforms state-of-the art anonymization pipelines with respect to image quality, resolution, Inception Score (IS) and Frechet Inception Distance (FID). Additionally, our method is invariant with respect to the image generator and thus able to be used with the latest models available.
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