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Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

Fahd N. Al-Wesabi1,*, Huda G. Iskandar2, Mohammad Alamgeer3, Mokhtar M. Ghilan2

1 Department of Computer Science, King Khalid University, KSA & Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
2 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
3 Department of Information Systems, King Khalid University, Mayahel Aseer, Saudi Arabia

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

Intelligent Automation & Soft Computing 2020, 26(6), 1267-1283.


In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect any tampering. INLPETWA approach has been implemented by self-developed program using PHP with VS code IDE. INLPETWA approach has been proved with various experiments and simulation scenarios. Comparison results with baseline approaches also show that the proposed approach is appropriate to detect all types of tampering attacks. The paper includes implications for integrating natural language processing and text-watermarking to propose an intelligent solution. This paper fulfils an identified need to study how we can use a robust text information via various Internet applications.


Cite This Article

F. N. Al-Wesabi, H. G. Iskandar, M. Alamgeer and M. M. Ghilan, "Proposing a high-robust approach for detecting the tampering attacks on english text transmitted via internet," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1267–1283, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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