Effect of Principal Component Analysis in Feature based Uncalibrated Steganalysis using Block Dependency

Abstract

Steganalysis is a domain where hidden information sent through a medium over the internet is detected. The medium can be text, audio, images or video. The steganalytic method is of two types- target and blind. In this paper, we utilize the blind steganalysis and extract the statistical changes that occur when an image is embedded. The statistical values are known as features. During the extraction of features, the irrelevant ones are also extracted, which may hamper the efficiency of the analysis. This paper checks the efficiency rate of steganalysis by eliminating irrelevant data. Principal component analysis is used for feature reduction. Discrete Cosine Transform is used. The features used here are a combination of different statistical features as first order, second order, and Markov. The features which include the inter-block dependency features, as well as the intra-block features are reduced.

Keywords: Steganalysis, Blind, Targeted, Feature reduction, Principal Component Analysis