ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving
This addresses data security concerns for autonomous driving systems by providing a dataset and method for desensitization, though it is incremental as it builds on existing detection and desensitization techniques.
The authors tackled the problem of protecting private information like faces and license plates in autonomous driving images by creating the first fisheye dataset (ADD) with 650K images and proposing a deep-learning framework, achieving effective desensitization as verified by experiments.
Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View(FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization.