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We are presenting a fast and innovative approach to performing approximatepattern-matching for plagiarism detection, using an NDFA-based approach thatsignificantly enhances performance compared to other existing similaritymeasures. We outline the advantages of our approach in the context ofblockchain-based non-fungible tokens (NFTs). We present, formalize, discuss andtest our proposed approach in several real-world scenarios and with differentsimilarity measures commonly used in plagiarism detection, and observesignificant throughput enhancements throughout the entire spectrum of tests,with little to no compromises on the accuracy of the detection process overall.We conclude that our approach is suitable and adequate to perform approximatepattern-matching for plagiarism detection, and outline research directions forfuture improvements.

A New High-Performance Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Based Non-Fungible Tokens (NFTs)