An Image Region Hierarchical Encryption Approach for Edge Intelligence Perception

In the context of smart cities, edge-aware machine vision systems are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing significant privacy risks. Therefore, it is necessary to encrypt images containing sensitive information. However, edge computational devices typically have limited computational ability. To address the need for privacy protection, this paper proposes a partial image encryption algorithm based on object detection. First, our approach uses an object detection model to identify private areas in images (such as license plates) and applies a specific encryption strategy to license plate areas. At the same time, the computational burden on edge devices is reduced. Additionally, we introduce a chaotic mapping algorithm based on image segmentation and compare its performance with traditional chaotic mapping algorithms. Experimental results show that the improved algorithm performs better in encrypting sensitive areas while also exhibiting superior performance in gray value histogram analysis and scatter plot analysis.