Vol.69, No.2, 2021, pp.1519-1536, doi:10.32604/cmc.2021.018202
OPEN ACCESS
ARTICLE
An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques
  • Fahd N. Al-Wesabi1,2,*
1 Department of Computer Science, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
2 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
* Corresponding Author: Fahd N. Al-Wesabi. Email:
Received 01 March 2021; Accepted 02 April 2021; Issue published 21 July 2021
Abstract
In this paper, the text analysis-based approach RTADZWA (Reliable Text Analysis and Digital Zero-Watermarking Approach) has been proposed for transferring and receiving authentic English text via the internet. Second level order of alphanumeric mechanism of hidden Markov model has been used in RTADZWA approach as a natural language processing to analyze the English text and extracts the features of the interrelationship between contexts of the text and utilizes the extracted features as watermark information and then validates it later with attacked English text to detect any tampering occurred on it. Text analysis and text zero-watermarking techniques have been integrated by RTADZWA approach to improving the performance, accuracy, capacity, and robustness issues of the previous literature proposed by the researchers. The RTADZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark. RTADZWA has been implemented using PHP with VS code IDE. The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion, reorder, and deletion attacks, e.g., Comparison results with baseline approaches also show the advantages of the proposed approach.
Keywords
Text analysis; NLP; hidden markov model; zero-watermarking; content authentication; tampering detection
Cite This Article
Al-Wesabi, F. N. (2021). An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques. CMC-Computers, Materials & Continua, 69(2), 1519–1536.
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