Trustworthy Semantic Communication for Vehicular Networks: Challenges and Solutions

2025-09-25

AI Summary

To address three critical trust challenges in vehicular semantic communication—unsecured information transmission, vulnerability of semantic encoders to adversarial attacks, and difficulty in assessing entity trustworthiness—this paper proposes a three-layer trustworthy semantic communication architecture. First, an adversarial-noise-based semantic camouflage transmission mechanism enables proactive eavesdropping prevention. Second, a poisoning-resistant federated learning framework for encoder-decoder training enhances model robustness and privacy preservation. Third, a trust evaluation mechanism integrating distributed auditing and game-theoretic analysis supports dynamic, quantifiable vehicle trust assessment. The solution incorporates V2X-optimized lightweight semantic coding and security protocols. Experimental validation in realistic scenarios demonstrates significant improvements: +28.6% malicious node detection rate, −31.4% end-to-end latency, and −42.1% bit error rate—establishing a systematic, end-to-end solution for trustworthy semantic communication in vehicular networks.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Agent Communication
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Semantic communication (SemCom) has the potential to significantly reduce communication delay in vehicle-to-everything (V2X) communications within vehicular networks (VNs). However, the deployment of vehicular SemCom networks (VN-SemComNets) faces critical trust challenges in information transmission, semantic encoding, and communication entity reliability. This paper proposes an innovative three-layer trustworthy VN-SemComNet architecture. Specifically, we introduce a semantic camouflage transmission mechanism leveraging defensive adversarial noise for active eavesdropping defense, a robust federated encoder-decoder training framework to mitigate encoder-decoder poisoning attacks, and an audit game-based distributed vehicle trust management mechanism to deter untrustworthy vehicles. A case study validates the effectiveness of the proposed solutions. Lastly, essential future research directions are pointed out to advance this emerging field.

Problem

• Addresses trust challenges in semantic communication for vehicular networks
• Mitigates security threats like eavesdropping and encoder-decoder poisoning attacks
• Ensures reliable communication entity trustworthiness in V2X environments

Innovation

• Semantic camouflage transmission with defensive adversarial noise
• Robust federated encoder-decoder training against poisoning attacks
• Audit game-based distributed vehicle trust management mechanism