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DocCompare: an approach to prevent the problem of character injection in document similarity algorithm
Journal article   Open access   Peer reviewed

DocCompare: an approach to prevent the problem of character injection in document similarity algorithm

Anupama Namburu, Akhil Surendran, S. Vijay Balaji, Senthilkumar Mohan and Celestine Iwendi
Mathematics, Vol.10(22), 4256
14/11/2022

Abstract

time-series modeling Deep learning AutoML data drift Machine Learning
There is a constant rise in the amount of data being copied or plagiarized because of the 1 abundance of content and information freely available across the internet. Even though the systems 2 try to check documents for the plagiarism, there have been trials to overcome these system checks. 3 In this paper, a concept of character injection used to trick the plagiarism is presented. It is also 4 showcased that how the the similarity check algorithms based on k-grams fails to detect the character 5 injection. In order to eradicate the problem or error in similarity rates caused due to the problem of 6 character injection, image processing based approach of multiple histogram projections are used. An 7 application is developed to detect the character injection in the document and produce the accurate 8 similarity rate. The results are shown with some test documents and the proposed method eliminates 9 any kind of character injected in the document that tricks the plagiarism. The proposed method has 10 addressed the problem of character injection with image processing based changes in the existing 11 methods of document-similarity check algorithms based on k-grams.
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