PLAGIARISM DETECTION FOR PROJECT REPORT USING MACHINE LEARNING

Abstract

Plagiarism is an unethical act of using someone else's work or ideas without giving them credit, which is a growing problem in various fields. However, the current systems for plagiarism detection require revealing the full content of input documents and document collections, which can raise procedural and legal concerns regarding data confidentiality, limiting or prohibiting the use of plagiarism detection services. To address these issues, we aim to create a plagiarism detection approach that doesn't need a centralized provider or expose any content as cleartext. Our research has produced initial results showing that our content-protecting method achieves the same detection effectiveness as the original method while making it practically impossible to reveal the protected content through common attacks. Various techniques, such as manual detection, text similarity analysis, and automated plagiarism detection using machine learning, have been developed to prevent plagiarism. This paper focuses on machine learning techniques for plagiarism detection and discusses different approaches, algorithms, and datasets used in detecting plagiarism, along with their advantages and limitations. The paper also presents some future research directions in this area.