Data Hiding in the Wild: Where Computational Intelligence Meets Digital Forensics

Abstract

In the context of an increasing dependence on multimedia contents, data hiding techniques, such as watermarking and steganography, are becoming more and more important. Due to the complementary nature of their general requirements, i.e., imperceptibility, robustness, security and capacity, many data hiding schemes endeavour to find the optimal performances by applying various approaches inspired from nature. In this paper, we provide a review and analysis of the main computational intelligence approaches, including Artificial Neural Networks (ANNs) and Fuzzy Sets (FSs), which are employed in information hiding. Furthermore, with the aid of the recent state of the art, we discuss the main challenges to be addressed and future directions of research.