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

被引:35
|
作者
Lin, Yijing [1 ]
Du, Hongyang [2 ]
Niyato, Dusit [2 ]
Nie, Jiangtian [2 ]
Zhang, Jiayi [4 ]
Cheng, Yanyu [2 ,3 ]
Yang, Zhaohui [5 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[3] Alibaba NTU Singapore Joint Res Inst, Singapore 639798, Singapore
[4] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[5] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310058, Peoples R China
基金
新加坡国家研究基金会;
关键词
Semantics; Blockchains; Metaverse; Transportation; Artificial intelligence; Image edge detection; Security; blockchain; semantic communication; semantic attacks; semantic defenses; INTERNET;
D O I
10.1109/OJCS.2023.3260732
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:72 / 83
页数:12
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