Aug 2026· Bulletin of Electrical Engineering and Informatics· Vol 15, pp. 3719-3731· 0 citations· 27 references
TL;DR
This study introduces an external plagiarism detection framework built on an artificial neural network model and a lexical feature extraction framework adapted to the linguistic features of Arabic, verifying its effectiveness for Arabic plagiarism detection.
Abstract
Plagiarism detection is the task of determining whether a document contains parts from other documents by employing different styles of plagiarism, such as copying certain parts and reordering or replacing words with synonyms, without citing the original text owner. This task is important in many applications, and there are two primary types of plagiarism detection methods: external and intrinsic. Plagiarism detection in Arabic documents is challenging because of Arabic’s rich morphological features, lexical variation, and syntactic complexity, which limit the effectiveness of some detection approaches. To address these challenges, this study introduces an external plagiarism detection framework built on an artificial neural network (ANN) model and a lexical feature extraction framework adapted to the linguistic features of Arabic. The proposed framework is evaluated using ExAraPlagDet-2015 benchmark, where a baseline model using support vector machine (SVM) is introduced for comparison. Experimental results demonstrate notable improvements in plagiarism detection performance of the proposed framework compared with SVM and other baseline methods. The proposed framework provides a precision value of 92% and an F-score value of 96%, verifying its effectiveness for Arabic plagiarism detection.
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
Hanan Mohammed Fawzy, Ahmad Salah, Heba El-Fiqi et al.· Informatica· 0 citations
A hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN) that enhances detection by modeling structural relationships within text data is proposed.
This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language, demonstrating that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages.
Muhammad Rizky, Anandita Priatama, Aviv Yuniar Rahman et al.· Buana Information Technology...· 0 citations
Experimental results indicate that the proposed approach achieves competitive performance compared to existing plagiarism detection systems, and the comparative analysis highlights the strengths and limitations of different word embedding models across datasets.
Malya Singh, Vishal Gupta· Knowledge and Information Sy...· 0 citations
This paper evaluates plagiarism detection using deep learning and natural language processing (NLP) techniques. A novel model, Detecting Embedded Plagiarized Text in Images (DEPTI), is introduced to identify plagiarized text embedded within images, demonstrating high accuracy and robust performance. DEPTI effectively recognizes paraphrased, translated, and artificial intelligence generated content, achieving strong detection capabilities across diverse scenarios. The model integrates PAN-PC-11, TF-IDF, Tesseract OCR, DistilBERT, and LSTM to extract and analyze text from images, enabling advanced plagiarism detection beyond conventional approaches. Experimental results confirm DEPTI’s effectiveness, highlighting its potential as a reliable tool for safeguarding academic integrity in the digital era.
B. Zaqaibeh, Ahmad Alhami· International journal of adv...· 0 citations
The rapid advancement of generative Artificial Intelligence (AI) presents challenges to academic integrity due to potential misuse like plagiarism. This study develops a text detection system specifically for the Indonesian language using a Deep Learning approach with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. The research methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A dataset comprising 5,008 text rows was compiled via web scraping from journalism platforms and academic journals indexed in SINTA 4 for human-written texts, while AI-generated counterparts were engineered using ChatGPT and Google Gemini paraphrases. Text features were extracted using a Keras Tokenizer and Embedding Layer with 64 dimensions. Evaluation of the trained Bi-LSTM model on a 30% validation split demonstrated an overall accuracy of 78.24% and a Mean Absolute Error (MAE) of 0.3295. Specifically, the model achieved a 93.77% success rate in identifying human-written texts, though it logged a lower detection rate of 62.62% for academic AI text structures. The final model was successfully deployed as a web application using Streamlit.
Rafil Moehamad Alif, Syariful Alam, Chandra Dewi Lestari· Jurnal Teknologi Informatika...· 0 citations