Tensor-Based GAN to Defense Adversarial Attacks for Cyber-Physical-Social System

Zhu, Yuxiang; Yang, Laurence T.; Jun, Feng; Xie, Xing

doi:10.1109/tnse.2021.3077305

Cited by 4 publications

(1 citation statement)

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“…1) Data Reliability of AI-generated Content, Digital Twin, and Physical Input: In the metaverse, AI such as generative adversarial network (GAN) can help generate high-quality dynamic game scenarios and context images in the metaverse, but also poses security threats such as adversarial threats which is hard to detect for humans. Zhu et al [76] propose a tensor-based adversarial training to resist adversarial samples in AI model training and improve learning robustness by taking adversarial samples as part of training data, which can be beneficial to resist adversarial threats in the scene construction in the metaverse.…”

Section: B Data Managementmentioning

confidence: 99%

A Survey on Metaverse: Fundamentals, Security, and Privacy

wang¹,

Zhou²,

Zhang³

et al. 2022

Preprint

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<div>Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from the science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity.</div><div>In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse.</div><div>Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.</div>

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Section: B Data Managementmentioning

confidence: 99%