journal article·2022Open access

An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

Anwer Mustafa Hilal, Fahd N. Al-Wesabi, Mohammed Alamgeer, Manar Ahmed Hamza, Mohammad Mahzari, Murad A. Almekhlafi

Computers, Materials & Continua· Volume 70· Issue 3

Abstract

Due to the rapid increase in the exchange of text information via internet networks, the security and authenticity of digital content have become a major research issue. The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text. In this paper, an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on the Arabic text transmitted online via an Internet network. Towards this purpose, the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches. In the proposed method, both embedding and extracting of the watermark are logically implemented, which causes no change in the digital text. This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark. This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text. An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy, robustness, and effectiveness under multiple random locations of the common tampering attacks.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Steganography and Watermarking Techniques · Internet Traffic Analysis and Secure E-voting · Chaos-based Image/Signal Encryption

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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