基于实例分割的车联网图像数据脱敏方法

Shuang Li, Yue Zhou, Xin Zhang, Meng Zhang
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引用次数: 0

摘要

随着智能网联汽车产业的不断发展,摄像头等车载设备被广泛使用,数据采集量也在不断增加。车联网产生的图像数据中隐藏着大量的敏感信息。数据泄露事件一旦发生,可能会造成非常严重的后果。为了提高车联网数据的安全性,降低图像数据中敏感信息泄露的威胁,本文提出了一种车联网图像数据的脱敏处理方法,并基于实例分割技术对敏感信息进行脱敏处理。本文以真实道路图像数据集为基础,基于所提出的框架实现了修改后数据集的脱敏。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Desensitization method of image data in the Internet of Vehicles based on instance segmentation
With the continuous development of intelligent connected vehicle industry, cameras and other vehicle-mounted devices are widely used, so the amount of data collection is increasing. There is a large amount of sensitive information hidden in the image data generated by connected vehicles. Once the data leakage event occurs, it may cause very serious consequences. In order to improve the security of connected vehicle data and reduce the threat of sensitive information leakage in image data, this paper provides a desensitization process of connected vehicle image data, and desensitizes sensitive information based on instance segmentation technology. In this paper, a real road image dataset is collected, and realizes desensitization of the modified dataset based on proposed framework.
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