Detecting Machine-Generated Arabic Text: AraBERT–LSTM for Trustworthy Low-Resource NLP

Deepfake text generation has emerged as a serious challenge in the age of advanced language models, particularly in low-resource languages like Arabic. This study presents a deep learning-based approach to detect synthetic Arabic text generated by AI systems. We propose a binary classification framework combining AraBERT embeddings with a Long Short-Term Memory (LSTM) network. A balanced dataset of 87,452 samples was constructed using real Arabic text and synthetic text generated via AraGPT2. Our best-performing model achieved a test accuracy of 99.5%, demonstrating strong generalization and detection capability. This work contributes to enhancing Arabic NLP security and offers a foundation for future multilingual deepfake detection systems. This version is archived in the Arab International University (AIU) repository for open access and dissemination purposes. The content of this paper has not been modified from the original publication.For more information, please visit the official repository of Arab International University (AIU): aiu.edu.sy Files