用AI实时识别工业元宇宙中伪造的NFT数字孪生
Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins
- 结合自编码器与RNN的深度学习模型，实现动态模式识别
- 通过动态元数据验证，有效检测95%以上伪造数字孪生
- 适合关注区块链资产安全的工业元宇宙开发者
工业元宇宙的兴起使数字孪生（DTs）成为核心。基于区块链的非同质化代币（NFT）为可复制的数字孪生提供去中心化所有权机制。然而，未经授权的复制（即伪造）严重威胁NFT-DTs的安全性。现有检测方法多依赖易被篡改的静态信息（如元数据和图像）。为此，我们提出一种结合自编码器与RNN分类器的深度学习方案，实现对虚假NFT-DTs的实时模式识别。此外，引入动态元数据概念，通过集成AI的智能合约提供更可靠的真伪验证。该系统能有效识别伪造数字孪生，增强元宇宙中基于NFT资产的安全性。
原文摘要 · Abstract (English)
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.
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