Open Access

ARTICLE

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Mohammad Alamgeer¹, Fahd N. Al-Wesabi^2,3,*, Huda G. Iskandar^3,4, Imran Khan⁵, Nadhem Nemri⁶, Mohammad Medani⁶, Mohammed Abdullah Al-Hagery⁷, Ali Mohammed Al-Sharafi^3,8

1 Department of Information Systems, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
2 Department of Computer Science, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
3 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
4 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
5 Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan
6 Department of Information Systems, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
7 Department of Computer Science, College of Computer, Qassim University, Saudi Arabia
8 Department of Computer Science, College of Computers and Information Technology, University of Bisha, KSA

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

Computers, Materials & Continua 2022, 71(2), 2497-2513. https://doi.org/10.32604/cmc.2022.018591

Received 12 March 2021; Accepted 13 April 2021; Issue published 07 December 2021

Abstract

Content authentication, integrity verification, and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology. In this paper, a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking (SFASCDW) is proposed for content authentication and tampering detection of English text. A first-level order of alphanumeric mechanism, based on hidden Markov model, is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach. The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text. Moreover, he extracts the features of the interrelationship among the contexts of the text, utilizes the extracted features as watermark information, and validates it later with the studied English text to detect any tampering. SFASCDW has been implemented using PHP with VS code IDE. The robustness, effectiveness, and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks, namely insertion, reorder, and deletion. The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

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Keywords

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