Open Access

ARTICLE

Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

Fahd N. Al-Wesabi^1,2,*
1 Department of Computer Science, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
2 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
* Corresponding Author: Fahd N. Al-Wesabi. Email:

Computers, Materials & Continua 2021, 67(3), 3701-3719. https://doi.org/10.32604/cmc.2021.015785

Received 06 December 2020; Accepted 11 January 2021; Issue published 01 March 2021

Abstract

Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents. The proposed approach embeds and detects the watermark logically without altering the original English text document. Based on hidden Markov model (HMM), the fourth level order of the word mechanism is used to analyze the contents of the given English text to find the interrelationship between the contexts. The extracted features are used as watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, the proposed approach has been implemented and validated with attacked English text. Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion, reorder, and deletion attacks. The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks. Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.

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Keywords

Text analysis; English language processing; hidden Markov model; zero-watermarking; content authentication; tampering detection

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