Digital Text Authentication Using Deep Learning: Proposition for the Digital Quranic Text

Abstract

Nowadays, the detection of digital text manipulation is a hot topic in natural language processing and artificial intelligence. This type of text spreads quickly and inexpensively, which can cause great concern due to its negative impact on social life. The text authentication process has gained a great deal of interest. However, the authentication of Arabic texts is still under development. The Quran is one of the Arabic texts sensitive to change and the most vulnerable to falsification. In order to prevent misuse of this type of text, in this research, a deep learning approach based on the LSTM network and the pretrained Word Embeddings has been developed for authentication one of the manipulations types of the Arabic Quranic texts. By building a model that automatically enables Internet users to validate the Quran content's arrangement, the experimental results showed that the proposed approach could improve text classification accuracy and achieve a significant time difference compared to previous works.