为自动驾驶街景图像设计隐私保护框架，隐藏人脸外的敏感信息
SVIA: A Street View Image Anonymization Framework for Self-Driving Applications
- 分三步：分割图像功能区域、生成替代内容、融合修补保持视觉一致
- 在两个公开数据集上五项指标均优于现有方法，兼顾画质与隐私
- 适合需要保护行人、车辆和位置信息的自动驾驶系统开发者
近年来，图像匿名化研究日益关注人脸和个体的去标识化。然而，在自动驾驶应用中，仅去除人脸和个体可能不足以提供充分的隐私保护，因为街景中的车辆、建筑等仍可能暴露位置、轨迹等敏感信息。因此，有必要将匿名化技术扩展至街景图像，以全面保护用户、行人和车辆的隐私。本文提出一种面向自动驾驶应用的街景图像匿名化框架（SVIA）。该框架包含三个核心组件：语义分割器用于将输入图像划分为功能区域，修复器用于生成隐私敏感区域的替代内容，调和模块用于无缝拼接修改后的区域以保证视觉一致性。实验结果表明，相比现有方法，SVIA在两个常用公共数据集上的五项常见指标上实现了更优的图像生成质量与隐私保护平衡。
原文摘要 · Abstract (English)
In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely de-identifying faces and individuals might not provide sufficient privacy protection since street views like vehicles and buildings can still disclose locations, trajectories, and other sensitive information. Therefore, it remains crucial to extend anonymization techniques to street view images to fully preserve the privacy of users, pedestrians, and vehicles. In this paper, we propose a Street View Image Anonymization (SVIA) framework for self-driving applications. The SVIA framework consists of three integral components: a semantic segmenter to segment an input image into functional regions, an inpainter to generate alternatives to privacy-sensitive regions, and a harmonizer to seamlessly stitch modified regions to guarantee visual coherence. Compared to existing methods, SVIA achieves a much better trade-off between image generation quality and privacy protection, as evidenced by experimental results for five common metrics on two widely used public datasets.
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