Physical-Layer Secure Transmission for Semantic Communication Systems

As a promising paradigm for the sixth-generation (6G) networks, task-oriented semantic communication significantly enhances transmission efficiency. However, it faces complex security challenges, particularly the risk of eavesdropping due to the open nature of wireless channels. To address this issue, we propose a secure semantic communication framework that integrates physical-layer secure beamforming (SBF) to safeguard semantic information from eavesdropping. Specifically, we design an SBF network to generate SBF vectors that focus signal beams on legitimate users to enhance signal power while directing designed artificial noise toward potential eavesdroppers to strengthen jamming. To further improve system adaptability across varying channel conditions, we introduce attention-based channel-aware modules that dynamically optimize the encoding, decoding, and beamforming processes based on perceived channel state information. Furthermore, task-oriented artificial noise is employed to degrade the task performance of eavesdroppers more effectively. Finally, a stepwise training strategy with task-specific loss functions is employed to jointly optimize the SBF and semantic modules, maximizing the performance gap in downstream tasks between legitimate users and eavesdroppers. The simulation results demonstrate that the proposed approach effectively maintains task performance for legitimate users while significantly suppressing eavesdroppers, outperforming conventional methods.