Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins

TL;DR

This paper presents a deep learning approach combining autoencoders and RNNs to detect counterfeit NFT digital twins in the industrial metaverse, enhancing security through real-time pattern recognition and dynamic metadata verification.

Contribution

It introduces a novel AI-based method for real-time detection of counterfeit NFT digital twins, addressing limitations of static metadata reliance.

Findings

Effective counterfeit detection with high accuracy

Real-time pattern recognition capability

Enhanced security via dynamic metadata verification

Abstract

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.