Arabic Text Steganography Based on Deep Learning Methods

Abstract

Steganography is one of the oldest methods for securely sending and transferring secret information between two people without raising suspicion. Recently, the use of Artificial Intelligence (AI) has become simpler and more widely used. Since the emergence of natural language processing (NLP), building language models using deep learning has become more. Furthermore, because of the importance of concealing secret information in delivered messages, Artificial Intelligence theories along with Natural Language Processing algorithms were employed to conceal secret information within the text cover. The Arabic language was used because of its large number of words, vocabulary, and linguistic meanings, and its most significant feature is Arabic poetry. This study discovered a new way to hide secret data inside newly formulated Arabic poetry based on previous Arabic poetic texts and a database of a number of Arab poets from the ancient and modern eras using Artificial Intelligence and Long Short-Term Memory (LSTM) theories to increase storage capacity by 45 percent. The linguistic accuracy and volume of secret data hidden within the formulated poetry were increased using a Baudot Code algorithm, where the secret data is hidden at the level of letters rather than words, and the linguistic accuracy and volume of secret data hidden within the formulated poetry were increased to eliminate the drawbacks found in previous studies.

Keywords

• Text steganography
• hiding information
• letter frequency
• LSTM
• Baudot code