Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins
This addresses security threats for NFT-based assets in the industrial metaverse, but it is incremental as it builds on existing NFT clone detection methods.
The paper tackles the problem of unauthorized duplication of NFT digital twins in the industrial metaverse by proposing a deep-learning-based solution combining an autoencoder and RNN classifier for real-time pattern recognition, achieving effective detection of counterfeit assets.
The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.