Abstract
Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, a Robust English Text Watermarking and Natural Language Processing Approach (RETWNLPA) is proposed based on word mechanism and first level order of Markov model to improve the accuracy of tampering detection of sensitive English text. The RETWNLPA approach embeds and detects the watermark logically without altering the original text document. Based on the hidden Markov model (HMM), the first-level order of word mechanism is used to analyze the interrelationship between English text. The extracted features are used as watermark information and integrated with text zero-watermarking techniques. To detect eventual tampering, RETWNLPA has been implemented and validated with attacked English text. Experiments were performed on four datasets of varying sizes under random locations of common tampering attacks. The simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks. Comparison results show that RETWNLPA outperforms baseline approaches HNLPZWA (an intelligent hybrid of natural language processing and zero-watermarking approach) and ZWAFWMMM (Zero-Watermarking Approach based on Fourth level order of Word Mechanism of Markov Model) in terms of tampering detection accuracy.