Abstract: 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.

El Mostafa Hambi and Faouzia Benabbou, “A New Online Plagiarism Detection System based on Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110956

@article{Hambi2020,
title = {A New Online Plagiarism Detection System based on Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110956},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110956},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {El Mostafa Hambi and Faouzia Benabbou}
}