Semantic Communication Physical Layer Security Performance Analysis

Semantic communication based on deep learning technology extracts the meaning of the transmitted source to achieve spectrum resource savings and enhance anti-interference capabilities. The black-box nature of deep learning models increases the difficulty for eavesdroppers in intercepting information, thereby significantly improving transmission security under normal circumstances. However, eavesdroppers may utilize model theft techniques to acquire models that have similar functionality and performance to the victim’s model. Moreover, even if all users are legitimate, the private information of a specific legitimate user should not be accessible to other users, and this issue also requires attention. As the lowest layer of wireless communication, the physical layer has been proven to enhance the security performance of communication systems by leveraging the randomness of physical channels. Unlike traditional communication, which transmits bit streams, semantic communication transmits semantic streams. Therefore, this paper converts semantic streams into bit streams to analyze the security performance of semantic communication using traditional communication metrics. Specifically, this paper assumes that the performance of the eavesdropper’s stolen model is consistent with that of the original model (although this assumption is impossible) and conducts a derivation analysis of the Secrecy Outage Probability (SOP).