Rough set theory based on robust image watermarking

Abstract

Computational intelligence involves convenient adaptation and self-organization concepts, theories, and algorithms, which provide appropriate actions for a complex and changing environment. Fuzzy systems, artificial neural networks, and evolutionary computation are the main computational intelligence approaches used in applications. Rough set theory is one of the important fuzzy systems that have a significant role in extracting rough information from vague and uncertain knowledge. It has a pivotal role in many vague problems linked to image processing, fault diagnosis, intelligent recommendation, and intelligent support decision-making. Image authentication and security are one of the essential demands due to the rapid evolution of tele-image processing systems and to the increase of cyberattacks on applications relying on such systems. Designing such image authentication and security systems requires the analysis of digital image characteristics which are, in majority, based on uncertain and vague knowledge. Digital watermarking is a well-known solution for image security and authentication. This chapter introduces intelligent systems based on image watermarking and explores the efficiency of rough set theory in designing robust image watermarking with acceptable rate of imperceptibility and robustness against different scenarios of attacks.