Plagiarism: Taxonomy, Tools and Detection Techniques
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2018.
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Citing papers per year: 2022: 3; 2023: 2.
5 papers.
- 2023A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Textscited by 23
- 2023Paraphrase Detection: Human vs. Machine Contentcited by 12
- 2022A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languagescited by 1
- 2022Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approachcited by 0
- 2022Duplicate Detection as a Servicecited by 0
- 2023A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts14 Nov 2023
- 2023Paraphrase Detection: Human vs. Machine Content24 Mar 2023
- 2022Duplicate Detection as a Service20 Jul 2022
- 2022Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approach25 Mar 2022
- 2022A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages10 Jan 2022