Metaverse中人工智能生成内容的区块链辅助安全语义通信

Yijing Lin;Hongyang Du;Dusit Niyato;Jiangtian Nie;Jiayi Zhang;Yanyu Cheng;Zhaohui Yang
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引用次数: 16

摘要

虚拟交通网络的构建需要从边缘设备向虚拟服务提供商(VSP)传输大量数据,以促进Metaverse中物理域和虚拟域之间的流通。利用语义通信来减少信息冗余,VSP可以从边缘设备接收语义数据,通过高级技术(例如AI生成内容(AIGC))为用户提供各种服务,以探索数字世界。但语义通信的使用引发了一个安全问题,因为攻击者可以发送具有相似语义信息但所需内容不同的恶意语义数据,以破坏Metaverse服务并导致AIGC的错误输出。因此,在本文中,我们首先为虚拟交通网络中的AIGC服务提出了一个区块链辅助语义通信框架,以促进VSP和边缘设备之间物理域和虚拟域的交互。我们展示了一种基于训练的目标语义攻击方案,通过各种损失函数生成对抗性语义数据。我们还设计了一个语义防御方案,该方案使用区块链和零知识证明来区分对抗性和真实性语义数据的语义相似性,并检查语义数据转换的真实性。仿真结果表明,与攻击方案相比,该防御方法可以将对抗性语义数据与真实语义数据的语义相似度降低30%。
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Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse
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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