A method for generating an NFT to guarantee the uniqueness of a digital image

A unique NFT image is generated through a hash algorithm and a dual-module generation model, which solves the problems of NFT's inability to guarantee uniqueness and high gas fees, realizes decentralized storage and verification, and ensures the uniqueness and security of digital assets.

CN120611366BActive Publication Date: 2025-10-10SHENZHEN MSU-BIT UNIVERSITY
2 Cites 0 Cited by

Patent Information

Application Number
CN202511107640.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-08
Publication Date
2025-10-10
Estimated Expiration
2045-08-08

AI Technical Summary

Technical Problem

Existing NFT technology cannot effectively guarantee the uniqueness of digital assets, and has high gas fees and dependence on external storage systems, resulting in high transaction costs and risk of data loss.

Method used

A hash algorithm is used to generate a hash value of the user's unique information. A dual-module generation model is combined to extract feature vectors from the NFT image dataset. A unique image is generated through a decoder and stored in a decentralized network to mint NFTs. The hash value is used to verify the uniqueness of the asset.

Benefits of technology

It ensures the uniqueness of digital assets in a decentralized network, reduces storage costs, solves the problems of NFT duplication and homogenization, and improves the security and efficiency of transactions.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses an NFT generation method for guaranteeing the uniqueness of digital images, comprising the following steps: step one, obtaining user information B of a user on a blockchain; step two, encoding the user information B by using a hash algorithm F to generate a hash value H; step three, loading a double-module generation model C1; step four, extracting a feature vector from an NFT data set by using an encoder module in the double-module generation model C1; step five, using the hash value H to match the feature vector to obtain a new feature vector as an input of a decoder module in the double-module generation model C1; step six, generating an image I by using the decoder module in the double-module generation model C1; and step seven, storing the image I to a decentralized network to generate a credential CID and mint an NFT. The application focuses on strengthening the protection of the uniqueness of digital assets, realizes efficient extraction of image semantic information, effectively reduces the storage cost, and effectively solves the problems of NFT copying and homogenization.
Need to check novelty before this filing date? Find Prior Art