Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse

• Published in: IEEE open journal of the Computer Society Vol. 4; pp. 1 - 12
• Main Authors: Lin, Yijing, Du, Hongyang, Niyato, Dusit, Nie, Jiangtian, Zhang, Jiayi, Cheng, Yanyu, Yang, Zhaohui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Blockchain; Blockchains; Communication; Cryptography; Domains; Electronic devices; Image edge detection; Metaverse; Redundancy; Security; Semantic Attacks; Semantic Communication; Semantic Defenses; Semantics; Transportation; Transportation networks; Virtual networks
• DOI: 10.1109/OJCS.2023.3260732
• ISSN: 2644-1268

The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.