Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL). Compared to traditional separate source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and relief of "cliff effect". However, it is difficult to couple existing secure communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of this emerging technology. To this end, our paper proposes a novel method called DL-based joint protection and source-channel coding (DJPSCC) for images that can successfully protect the visual content of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is to use a neural network to conduct visual protection, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJPSCC method learns: 1) deep neural networks for image protection and image deprotection, and 2) an effective DJSCC network for image transmission in the protected domain. Compared to existing source protection methods applied with DJSCC transmission, the DJPSCC method achieves much better reconstruction performance.

翻译：联合源信道编码（JSCC）由于深度学习的引入取得了巨大成功。与传统的分离源信道编码（SSCC）方案相比，基于深度学习的JSCC（DJSCC）具有高频谱效率、高重构质量以及缓解"悬崖效应"等优势。然而，与传统的SSCC方案不同，现有的安全通信机制（如加密-解密机制）难以与DJSCC有效结合，这阻碍了这项新兴技术的实际应用。为此，本文提出了一种名为基于深度学习的联合保护与源信道编码（DJPSCC）的新方法，用于在不过度牺牲图像重构性能的前提下，成功保护明文图像的视觉内容。该设计思路是利用神经网络进行视觉保护，将明文图像转换为受视觉保护的图像，同时考虑其与DJSCC的交互作用。在训练阶段，所提出的DJPSCC方法学习：1）用于图像保护与图像去保护的深度神经网络，以及2）在保护域内进行图像传输的高效DJSCC网络。与现有结合DJSCC传输的源保护方法相比，DJPSCC方法实现了显著更优的重构性能。