ABSTRACT

The goal of steganography is to avoid drawing suspicion to the transmission of a secret message. This chapter provides an overview of steganalysis and examines the statistical properties of images. It discusses the visual steganalytic system and also examines a steganalytic system based on image quality measure (IQM). The chapter looks at learning strategies and explains a steganalytic system within the frequency domain. Statistical analysis of images can determine whether their statistical properties deviate from the expected norm. There are many types of learning strategies, including statistical learning; neural networks; and the support vector machine (SVM), which is used to generate an optimal separating hyperplane by minimizing the generalization error without using the assumption of class probabilities such as a Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called support vectors.