Semantic communication has emerged as a new deep learning-based communication paradigm that drives the research of end-to-end data transmission in tasks like image classification, and image reconstruction. However, the security problem caused by semantic attacks has not been well explored, resulting in vulnerabilities within semantic communication systems exposed to potential semantic perturbations. In this paper, we propose a secure semantic communication system, DiffuSeC, which leverages the diffusion model and deep reinforcement learning (DRL) to address this issue. With the diffusing module in the sender end and the asymmetric denoising module in the receiver end, the DiffuSeC mitigates the perturbations added by semantic attacks, including data source attacks and channel attacks. To further improve the robustness under unstable channel conditions caused by semantic attacks, we developed a DRL-based channel-adaptive diffusion step selection scheme to achieve stable performance under fluctuating environments. A timestep synchronization scheme is designed for diffusion timestep coordination between the two ends. Simulation results demonstrate that the proposed DiffuSeC shows higher robust accuracy than previous works under a wide range of channel conditions, and can quickly adjust the model state according to signal-to-noise ratios (SNRs) in unstable environments.

翻译：语义通信已成为一种基于深度学习的新型通信范式，推动了图像分类、图像重建等任务中端到端数据传输的研究。然而，由语义攻击引发的安全问题尚未得到充分探索，导致语义通信系统在潜在语义扰动下存在脆弱性。本文提出了一种名为DiffuSeC的安全语义通信系统，该利用扩散模型与深度强化学习（DRL）解决上述问题。通过发送端的扩散模块与接收端的非对称去噪模块，DiffuSeC能够缓解由数据源攻击和信道攻击等语义攻击引入的扰动。为提升在语义攻击导致的不稳定信道条件下的鲁棒性，我们开发了一种基于DRL的信道自适应扩散步长选择方案，以在波动环境中保持稳定性能。同时设计了一种时间步同步方案，用于协调两端的扩散时间步。仿真结果表明，所提出的DiffuSeC在多种信道条件下均比现有工作具有更高的鲁棒准确率，且能根据信噪比（SNR）在不稳定环境中快速调整模型状态。