Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels renders them vulnerable to eavesdropping, posing a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach, which embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Experiments also show that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach.

翻译：语义通信（SemCom）已成为面向即将到来的第六代（6G）网络的关键技术，其优势在于增强的通信效率以及对信道噪声的鲁棒性。然而，无线信道的开放性使其易受窃听威胁，对隐私构成严重风险。为解决此问题，我们提出了一种新颖的面向图像传输的安全语义通信（SemCom）方法，该方法集成隐写术技术，将隐私信息隐藏于非隐私图像（宿主图像）中。具体而言，我们提出了一种基于可逆神经网络（INN）的信号隐写方法，该方法在传输前将隐私图像的通道输入信号嵌入宿主图像的通道输入信号中。这确保了合法接收方能够从接收信号中重建原始隐私图像，而窃听者仅能解码宿主图像的信息。仿真结果表明，与无任何安全机制的场景相比，所提方法在合法接收方处对宿主图像和隐私图像均保持了可比较的重建质量。实验还表明，窃听者仅能重建宿主图像，展示了我们方法所增强的安全性。