Dynamic NFT Classification and Detection on Ethereum via Smart Contract*

Abstract

In recent years, Non-Fungible Token (NFT) has gradually become the key application of blockchain technology. Static NFT is the most common type of NFT. Once static NFT is minted on the blockchain, its additional metadata is immutable. However, some NFTs that mark real assets, games, sports, and other types must dynamically update the metadata. Therefore, a dynamic NFT with changeable features is needed. The emergence of dynamic NFT has greatly expanded the application innovation scene, and promoted the rapid development of community ecology, but also brought new problems and challenges to anti-fraud and supervision. This paper aims to realize the classification and detection of dynamic NFT. First, define and classify dynamic NFTs from both dynamic and static perspectives. Second, a complete dataset of dynamic NFT smart contract codes on Ethereum was constructed for the first time, and analyzed from multiple perspectives. Third, a smart contract feature model of dynamic NFT is proposed, and machine learning methods are used for recognition and classification. After experimental verification, the method proposed in this article can be effectively used to detect and identify dynamic NFTs, helping NFT holders avoid risks.