Open Access

ARTICLE

Tampering Detection Approach of Arabic-Text Based on Contents Interrelationship

by Fahd N. Al-Wesabi¹, Abdelzahir Abdelmaboud^2,*, Adnan A. Zain³, Mohammed M. Almazah⁴, Ammar Zahary⁵

1 Department of Computer Science, King Khalid University, Muhayel Aseer, KSA & Faculty of Computer and IT, Sana’a University, Yemen
2 Department of Information Systems, King Khalid University, Mayahel Aseer, KSA
3 Department of Electronic & Communication Engineering, Faculty of Engineering, University of Aden, Aden, Yemen
4 Department of Mathematics and Computer, IBB University, IBB, Yemen & Department of Mathematics, KKU, KSA
5 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen

* Corresponding Author: Abdelzahir Abdelmaboud. Email:

(This article belongs to the Special Issue: Artificial Techniques: Application, Challenges, Performance Improvement of Smart Grid and Renewable Energy Systems)

Intelligent Automation & Soft Computing 2021, 27(2), 483-498. https://doi.org/10.32604/iasc.2021.014322

Received 13 September 2020; Accepted 28 October 2020; Issue published 18 January 2021

Abstract

Text information depends primarily on natural languages processing. Improving the security and usability of text information shared through the public internet has therefore been the most demanding problem facing researchers. In contact and knowledge sharing through the Internet, the authentication of content and the identification of digital content are becoming a key problem. Therefore, in this paper, a fragile approach of zero-watermarking based on natural language processing has been developed for authentication of content and prevention of misuse of Arabic texts distributed over the Internet. According to the proposed approach, watermark embedding, and identification was technically carried out such that the initial text document did not need to be changed to embed a watermark. The automated zero-watermarking approaches have been combined with a second-tier word order framework based on the Markov model to boost the efficiency, precision, ability, and fragility of the existing researches. This second-tier of the Markov-model has been used as a natural language processing technique to analyze Arabic-text and extract the features of the interrelationship between textual contexts. Moreover, the extracted features have been utilized as information of watermark and then validated to identify any possible tampering with the attacked Arabic-text. The recommended solution has been applied with VS code IDE using PHP. The experimental results using four datasets of varying size show that the proposed approach can obtain better detection accuracy of tampering attacks, effectiveness and high fragility for common random insertion, reorder and deletion attacks for common attacks, e.g., Comparison results with baseline approaches also show the advantages of the proposed approach.

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