WeChat Mini Program
Old Version Features

    • Dynamic NFT Classification and Detection on Ethereum Via Smart Contract*

    Keting Yin,Zheng Zhu,Xiaoxue Ren, Xing Wang

    IEEE International Conference on Systems, Man and Cybernetics（2024）

    The School of Software Technology

    Cited 0|Views5
    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.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined