Balancing Security and Efficiency in GAI-Driven Semantic Communication: Challenges, Solutions, and Future Paths

Qianyun Zhang, Jiting Shi, Weihao Zeng, Xinyu Xu, Zhenyu Guan, Shufeng Li, Zhijin Qin

Abstract: The convergence of artificial intelligence (AI) and wireless communications has driven the emergence of semantic communication (SC), a paradigm that prioritizes context-aware semantic exchange over traditional bit-level transmission. Although enhancing efficiency and task-specific reliability, this advancing capability is accompanied by significant security challenges that remain underexplored. In this paper, we provide an overview of security challenges in SC systems, with a particular focus on the confidentiality, integrity, and availability of the wireless transmission and generative AI (GAI) models. To defend against risks of model confidentiality compromise and semantic feature leakage, we propose a solution integrating trusted execution environments (TEEs) for secure model inference and adversarial cryptography for the protection of semantics over realistic wireless channels. Test results show it achieves close-to-black-box attack resistance in model stealing effectiveness, and the BLEU scores of eavesdropping attackers are effectively reduced to below 0.1 across various SNR levels. Finally, we discuss potential open issues and solutions for enhancing the SC security, paving the way for future research in this critical area. The proposed framework demonstrates promising results in enhancing both model and data confidentiality, contributing to the development of secure SC systems for 6G networks.

External IDs:doi:10.1109/mnet.2025.3578783