NFT Image Verification Using Object Features and Denormalization
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Solution Overview
Problem
The growing popularity of non-fungible tokens (NFTs) has highlighted security risks and the need for effective protection of original NFTs, particularly in the context of digital assets like images, where existing methods lack efficient registration and verification processes.
Innovation Solution
A method and system utilizing a non-fungible token graphical user interface, verifier, object recognizer, characteristic extractor, denormalizer, and uniqueness purifier to register and verify images as unique NFTs by extracting characteristics, denormalizing images, and creating metadata, while minimizing computational impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NFT registration methods are used, then NFTs can be created and registered, but security risks increase and protection of original NFTs becomes difficult
Solution Approach 1:
The patent segments the NFT registration and verification process into distinct functional modules: image normalization module, object recognition module, characteristic extraction module, denormalization module, and uniqueness verification module. Each module performs a specific function, making the overall system more manageable and secure while maintaining complexity through modular architecture.
Solution Approach 2:
The patent applies preliminary action by normalizing images before processing and by pre-extracting characteristics of objects in the prioritized object queue. The system performs preliminary normalization and characteristic extraction before the main verification process, ensuring that images are prepared in advance for efficient and secure registration.
2Measurement precision
If comprehensive image processing is performed for NFT registration, then verification accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments image processing into prioritized queues of objects, processing only the most relevant objects first. The object recognition module creates a prioritized object queue based on NFT criteria, allowing the system to focus computational resources on the most important characteristics for verification accuracy.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of images through normalization and characteristic extraction, rather than analyzing every pixel and object equally. The system extracts characteristics from objects in the prioritized queue and denormalizes images selectively, achieving sufficient verification accuracy without excessive computational expenditure.
3Reliability
If multiple images are processed for registration, then NFT uniqueness can be verified, but service level impact and processing time increase
Solution Approach 1:
The patent performs preliminary normalization of images and pre-extraction of object characteristics before the main verification process. This preliminary action prepares the data in advance, allowing the uniqueness verification to proceed more quickly by working with pre-processed information rather than raw images.
Solution Approach 2:
The patent segments the verification process into parallel operations: normalization, object recognition, characteristic extraction, and denormalization can occur in parallel for multiple images. The prioritized object queue allows the system to process multiple images simultaneously, maintaining uniqueness verification reliability while reducing overall processing time.
Data Source
AI summary
A method and system for registering images as non-fungible token images include a non-fungible token verifier verifying that an image has not been registered as a registered non-fungible token. Once the image is verified, an object recognizer recognizes objects of the image that are classified into a object queue. A characteristic extractor then extracts one or more characteristics of the queued objects. A non-fungible token denormalizer then denormalizes the image into one or more denormalized images each having denormalization attributes. A uniqueness purifier then inspects objects of each of the denormalized images for characteristics extracted from objects in the queue, where matching characteristics between the objects of the denormalized images and objects of the image define unique parameters. Non-fungible token register then registers the image as a unique non-fungible token image. A non-fungible token verification method is carried out along with, or independent of, the non-fungible token registration method.