Open Access

ARTICLE

A Smart English Text Zero-Watermarking Approach Based on Third-Level Order and Word Mechanism of Markov Model

Fahd N. Al-Wesabi^{1, 2, *}

1 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia.
2 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen.

* Corresponding Author: Fahd N. Al-Wesabi. Email: .

Computers, Materials & Continua 2020, 65(2), 1137-1156. https://doi.org/10.32604/cmc.2020.011151

Received 22 April 2020; Accepted 08 June 2020; Issue published 20 August 2020

Abstract

Text information is principally dependent on the natural languages. Therefore, improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter. Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet. In this paper, an intelligent text Zero-Watermarking approach SETZWMWMM (Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model) has been proposed for the content authentication and tampering detection of English text contents. The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document. Based on Hidden Markov Model (HMM), Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SETZWMWMM has been implemented and validated with attacked English text. Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.

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Keywords

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