Plagiarism Detection Using Machine Learning

Abstract

Plagiarism has become a critical issue in academic and professional environments due to the easy availability of digital content. Traditional plagiarism detection techniques based on keyword matching and string similarity often fail to detect paraphrased and semantically similar content. This paper presents a machine learning-based plagiarism detection system that integrates natural language processing (NLP) techniques and cosine similarity for accurate detection of plagiarized content. The proposed system preprocesses text using tokenization, stop-word removal, and lemmatization, followed by feature extraction using TF-IDF vectorization. Online content is retrieved using Google Custom Search API and compared with user-submitted documents. Experimental results demonstrate that the proposed approach effectively identifies exact and paraphrased plagiarism with improved accuracy and efficiency. The system provides detailed plagiarism reports and visualizations, making it suitable for academic and professional use.