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

Abstract

AbstractNowadays, the detection of digital texts manipulation is a hot topic in the field of 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 Quranic text is one of the Arabic texts sensitive to change and the most vulnerable to falsification at all. In order to prevent misuse of this type of texts, in this research a deep learning approach based on 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 enables Internet users to automatically validate the arrangement of the Quran content, the experimental results showed that the proposed approach can effectively improve the accuracy of text classification and achieve a significant time difference compared to previous works.KeywordsDeep learningLSTMNatural language processingArtificial intelligenceAuthenticationIntegrityQuranic textArrangement