An Innovative NFT Approach to Ownership Assurance via Digital Content Similarity

Abstract

Non-Fungible Tokens (NFTs) have gained attention as a technology for guaranteeing ownership of digital content, leading to rapid market expansion. However, NFTs are limited in that they guarantee ownership only for a single, explicitly designated digital asset. For instance, if an image associated with an NFT undergoes modifications such as resolution reduction or trimming, it falls outside the scope of the NFT’s guarantee. In this study, we propose a new NFT scheme capable of guaranteeing ownership for multiple digital assets that fall within a defined visual similarity threshold. The core of this method lies in replacing conventional cryptographic hash functions with Image Hash functions, allowing the scope of ownership to cover a "range" of similar content rather than a single exact match. This enables highly similar content to be automatically included within the NFT’s scope of guarantee without explicit designation. To verify the feasibility of this scheme, we implemented and evaluated a prototype using four types of Image Hash functions against common image transformations, such as resolution reduction and trimming, on the Polygon blockchain. The results indicate that both Average Hash (aHash) and Perceptual Hash (pHash) are suitable functions, and that the NFT verification process can be performed efficiently. This method provides a novel mechanism to dynamically extend the scope of NFT ownership, paving the way for new potential applications for NFTs.