Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adversarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies.

翻译：语义通信对于下一代无线通信网络至关重要。现有研究已开发出基于深度学习的语义通信框架。然而，基于深度学习的系统容易受到后门攻击和对抗性攻击等威胁。本文深入研究了针对深度学习赋能的语义通信系统的后门攻击。鉴于现有的后门攻击工作并非针对语义通信场景量身定制，本文引入了一种面向语义符号的新型后门攻击范式（BASS），并基于此设计了相应的防御措施。具体而言，提出了一种用于防御BASS的训练框架。此外，还设计了基于逆向工程和基于剪枝的防御策略，以保护语义通信免受后门攻击。仿真结果表明了所提出的攻击范式及防御策略的有效性。