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

• URL: http://arxiv.org/abs/2301.11289v1
• Date: Wed, 25 Jan 2023 02:32:02 GMT
• Title: Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse
• Authors: Yijing Lin, Hongyang Du, Dusit Niyato, Jiangtian Nie, Jiayi Zhang, Yanyu Cheng, and Zhaohui Yang
• Abstract summary: We propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks. We illustrate a training-based 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.
• Score: 59.04428659123127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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