  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.
