Plagiarism detection has become increasingly challenging due to the widespread availability of paraphrasing tools and generative artificial intelligence systems. Traditional plagiarism detection techniques based on lexical similarity, such as TF-IDF and n-gram matching, often fail to identify semantically similar but lexically modified text. This paper presents a hybrid plagiarism detection framework that combines lexical similarity measures with semantic similarity derived from sentence transformer models. The proposed approach integrates TF-IDF-based cosine similarity with lightweight sentence embeddings generated using MiniLM and SBERT models. To enhance semantic detection performance, a MiniLM-based sentence transformer is fine-tuned on the PAN 2011 plagiarism detection corpus. Experimental evaluation demonstrates that the hybrid similarity approach significantly improves detection accuracy compared to purely lexical methods, particularly for paraphrased plagiarism cases. The framework is further validated using threshold-based analysis and real-world Web content retrieved through automated scraping. The proposed system provides an efficient and scalable solution for plagiarism detection, balancing computational efficiency with semantic understanding, and is suitable for academic and real-world forensic applications.
Rahul Birwadkar (Wed,) studied this question.