A new online plagiarism detection system based on deep learning
Open accessAbstract
The Plagiarism is an increasingly widespread and growing problem in the academic field. Several plagiarism techniques are used by fraudsters, ranging from a simple synonym replacement, sentence structure modification, to more complex method involving several types of transformation. Human based plagiarism detection is difficult, not accurate, and time-consuming process. In this paper we propose a plagiarism detection framework based on three deep learning models: Doc2vec, Siamese Long Short-term Memory (SLSTM) and Convolutional Neural Network (CNN). Our system uses three layers: Preprocessing Layer including word embedding, Learning Layers and Detection Layer. To evaluate our system, we carried out a study on plagiarism detection tools from the academic field and make a comparison based on a set of features. Compared to other works, our approach performs a good accuracy of 98.33 % and can detect different types of plagiarism, enables to specify another dataset and supports to compare the document from an internet search.
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Deep learning models such as different variants of convolutional and recurrent neural network architectures have also been used for plagiarism detection.
“More recently, deep learning models such as different variants of convolutional and recurrent neural network architectures have also been used for plagiarism detection (El Mostafa Hambi, 2020; El-Rashidy et al., 2022).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as El Mostafa Hambi, 2020
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- Authors
- Hambi, E. M., & Benabbou, F.
- Affiliations
- University of Hassan II Casablanca (MA)
- ORCID
- Benabbou, Faouzia
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2020
- Volume
- 11
- Issue
- 9
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W3089874376
- Cited by
- 18
- References
- 10
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 1
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Computer Science and Applications (publishedVersion, cc-by)
- International Journal of Advanced Computer Science and Applications (submittedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- plagiarism detection, academic integrity, Doc2Vec, convolutional neural networks, word embeddings
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