Vol.66, No.1, 2021, pp.195-211, doi:10.32604/cmc.2020.012088
A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet
  • Fahd N. Al-Wesabi1,2,*
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: falwesabi@kku.edu.sa
Received 14 June 2020; Accepted 30 June 2020; Issue published 30 October 2020
In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SAMMZWA has been implemented and validated with attacked Arabic 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 for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.
HMM; NLP; text analysis; zero-watermarking; tampering detection
Cite This Article
F. N. Al-Wesabi, "A hybrid intelligent approach for content authentication and tampering detection of arabic text transmitted via internet," Computers, Materials & Continua, vol. 66, no.1, pp. 195–211, 2021.
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.