Image Encryption Methods in Deep Joint Source Channel Coding: A Review and Performance Evaluation
Jialong Xu, Bo Ai, Wei Ren Chen, Ang Yang, Peng Sun · 2021 7th International Conference on Computer and Communications (ICCC) · 2021
With the introduction of the deep learning (DL), joint source and channel coding (JSCC) has achieved great success and has been demonstrated to have better performance especially in the case of low signal-to-noise ratios (SNRs) and limited bandwidth. As a promising wireless transmission technology, DL based JSCC (DJSCC) can serve different kinds of applications. However, some applications (e.g., biomedical, surveillance, financial and military) have strict requirements on data privacy and data security. Traditional image encryption methods are based on the shuffle operation and changing the intensity of each pixel in the plain image, which changes the ambient structure of the picture and degrades the reconstruct performance of the DJSCC. Perceptual image encryption methods for deep neural networks (DNNs) have been demonstrated to have similar classification accuracy in comparison to the DNNs trained on plain images. However, it is unknown how to conduct image encryption methods in the DJSCC framework. In this paper, we investigate different image encryption methods at first and compare their reconstruction performance. This research work provide a guidance for selecting the image encryption method suitable for DJSCC transmission.