A zero watermarking approach for content authentication and tampering detection of Arabic text based on fourth level order and word mechanism of Markov model

摘要

Text information is a natural language dependent. Therefore, improving security and reliability of text information exchanged via Internet network has become one of the most challenges faced by researchers. Content authentication, integrity verification and tampering detection of digital contents have become a major concern in the communication and information exchange area via the Internet.In this paper, an intelligent text Zero-Watermarking approach called ZWAFWMMM (Zero-Watermarking Approach based on Fourth level order of Word Mechanism of Markov Model) has been proposed for content authentication and tampering detection of Arabic text contents. In ZWAFWMMM, watermark embedding and tampering detection were performed logically in which no need to alter the original text document to embed a watermark. Fourth-level order of word mechanism based on hidden Markov model has been integrated with digital zero-watermarking techniques to improve performance, accuracy, capacity and robustness of the previous literature proposed by researchers. Moreover, Fourth-Level order of Markov model is used as a soft computing technique for analysing Arabic text and extracting the interrelationship features from the context of a text, and then utilizing the extracted features as a watermark information to be validated later with attacked Arabic text to detect any tampering that may occur in the text.ZWAFWMMM has been implemented by using PHP with VS code IDE. Robustness, effectiveness, and applicability of ZWAFWMMM are proved with several scenarios of simulations and experiments using four datasets of different lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experiment results show that ZWAFWMMM is more sensitive for all kinds of tampering attacks with a high-level accuracy in tampering detection. (C) 2020 Elsevier Ltd. All rights reserved.