Plagiarism Detection Using Machine Learning Techniques

Abstract

Plagiarism has become a major concern in academic and professional environments due to the easy availability of digital content. Traditional plagiarism detection methods such as keyword matching fail to identify paraphrased or semantically similar text. This paper proposes an intelligent plagiarism detection system using machine learning and Natural Language Processing techniques. The system preprocesses input documents, retrieves relevant online sources using Google Custom Search API, and measures similarity using TF-IDF vectorization and cosine similarity. The proposed approach effectively detects both direct and rephrased plagiarism. Experimental results demonstrate improved accuracy and reliability compared to conventional methods, making the system suitable for educational and content verification applications.