Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

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, text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents. The proposed approach embeds and detects the watermark logically without altering the original English text document. Based on hidden Markov model (HMM), the fourth level order of the word mechanism is used to analyze the contents of the given English text to find the interrelationship between the contexts. The extracted features are used as watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, the proposed approach has been implemented and validated with attacked English text. Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion, reorder, and deletion attacks. The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks. Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.