Semantic communication (SemCom) aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement learning (DRL)-empowered multi-level jamming approach to enhance the security of SemCom systems over MIMO fading wiretap channels. This approach combines semantic layer jamming, achieved by encoding task-irrelevant text, and physical layer jamming, achieved by encoding random Gaussian noise. These two-level jamming signals are superposed with task-relevant semantic information to protect the transmitted semantics from eavesdropping. A deep deterministic policy gradient (DDPG) algorithm is further introduced to dynamically design and optimize the precoding matrices for both task-relevant semantic information and multi-level jamming signals, aiming to enhance the legitimate user's image reconstruction while degrading the eavesdropper's performance. To jointly train the SemCom model and the DDPG agent, we propose an alternating optimization strategy where the two modules are updated iteratively. Experimental results demonstrate that, compared with both the encryption-based (ESCS) and encoded jammer-based (EJ) benchmarks, our method achieves comparable security while improving the legitimate user's peak signal-to-noise ratio (PSNR) by up to approximately 0.6 dB.

翻译：语义通信旨在仅传输与任务相关的信息，从而提高通信效率，但同时也将语义信息暴露于潜在的窃听风险之下。本文提出一种基于深度强化学习的多层级干扰方法，以增强语义通信系统在多输入多输出衰落窃听信道上的安全性。该方法结合了语义层干扰（通过对任务无关文本进行编码实现）与物理层干扰（通过对随机高斯噪声进行编码实现）。这两层级的干扰信号与任务相关的语义信息叠加，以保护所传输的语义免受窃听。进一步引入深度确定性策略梯度算法，动态设计与优化任务相关语义信息及多层级干扰信号的预编码矩阵，旨在提升合法用户的图像重建质量，同时降低窃听者的性能。为了联合训练语义通信模型与深度确定性策略梯度智能体，我们提出一种交替优化策略，其中两个模块迭代更新。实验结果表明，与基于加密的基准方法和基于编码干扰器的基准方法相比，我们的方法在实现相当安全性的同时，将合法用户的峰值信噪比提升了最高约0.6分贝。