Enhancing the Security of Semantic Communication via Knowledge-Aided Coding and Jamming

As semantic communication (SemCom) emerges as a promising communication paradigm, ensuring the security of semantic information over open wireless channels has become crucial. Traditional encryption methods introduce considerable communication overhead, while existing learning-based secure SemCom schemes often rely on a channel capacity advantage for the legitimate receiver, which is challenging to guarantee in practice. In this paper, we propose a coding-enhanced jamming approach that eliminates the need to transmit a secret key by utilizing shared knowledge between the legitimate receiver and the transmitter. We generate private codebooks with neural network (NN)-based encoders, using them to encode data into a sequence Y1, which is then superposed with a sequence Y2 drawn from the private codebook. By optimizing the power allocation between the two sequences, the legitimate receiver can successfully decode the data, while the eavesdropper' s performance is significantly degraded, potentially to the point of random guessing. Experimental results demonstrate that our method achieves comparable security to state-of-the-art approaches while significantly improving the reconstruction performance of the legitimate receiver by more than 1 dB across varying channel signal-to-noise ratios (SNRs) and compression ratios.