# Plagiarism: Taxonomy, Tools and Detection Techniques

arXiv: https://arxiv.org/abs/1801.06323 · HTML: https://mlsanity.com/paper/1801.06323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (2023)
- [A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages](https://mlsanity.com/paper/2201.03423/) (2022)
- [Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approach](https://mlsanity.com/paper/2203.13430/) (2022)
- [Duplicate Detection as a Service](https://mlsanity.com/paper/2207.09672/) (2022)

## Abstract (arXiv)

To detect plagiarism of any form, it is essential to have broad knowledge of its possible forms and classes, and existence of various tools and systems for its detection. Based on impact or severity of damages, plagiarism may occur in an article or in any production in a number of ways. This survey presents a taxonomy of various plagiarism forms and include discussion on each of these forms. Over the years, a good number tools and techniques have been introduced to detect plagiarism. This paper highlights few promising methods for plagiarism detection based on machine learning techniques. We analyse the pros and cons of these methods and finally we highlight a list of issues and research challenges related to this evolving research problem.