Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack

Abstract

In this article, a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet (HFDATAI) is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft computing. The HFDATAI solution technically integrates and senses the watermark without modifying the original text. The alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated, null-watermarking approach to enhance the proposed approach's efficiency, accuracy, and intensity. The first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of the Arabic language. In addition, the features of the interrelationship among text contexts and characteristics of watermark information extraction that is used later validated for detecting any tampering of the Arabic-text attacked. The HFDATAI strategy was introduced based on PHP with included IDE of VS code. Experiments of four separate duration datasets in random sites illustrate the fragility, efficacy, and applicability of HFDATAI by using the three common tampering attacks i.e., insertion, reorder, and deletion. The HFDATAI was found to be effective, applicable, and very sensitive for detecting any possible tampering on Arabic text.