Secure MLLM Semantic Communication Enabled Air-Space-Ground-Maritime Networks

With the advancement of 6G, future mobile networks are witnessing a seamless integration between terrestrial and non-terrestrial networks to establish a global air-space-ground-maritime network. The dynamic channel and vast geographic coverage in these networks require efficient transmission of the multi-modal data. Meanwhile, semantic communication is a promising solution for this issue since it focuses on meaning rather than symbol-level reconstruction. However, due to wireless transmission and additional vulnerabilities introduced by semantic processing, these networks face critical security challenges and open issues in both conventional communication and the semantic level. Therefore, we propose a secure multimodal semantic communication framework leveraging multimodal large language models (MLLMs) to address these challenges. We provide a systematic analysis of three security aspects: security in system and model, security in data and semantics, and security in communication and transmission. In addition, we have developed a novel framework that integrates semantic representation with joint source-channel coding (JSCC) for semantic information transmission. Then, a comprehensive protection mechanism is proposed that jointly optimizes security at both physical and semantic layers through artificial noise. Finally, experimental results demonstrate that our approach enhances the efficiency and security of communication. It shows that the achievable secrecy rate is improved by up to 40 percent while maintaining a gap exceeding 10 dB between the legitimate receiver and eavesdropper.