Comparative Analysis of Filter Feature Selection Techniques with Different Classifiers for Image Steganalysis

Steganalysis aims at detecting the presence of steganography with or without knowledge of steganographic technique used. Feature based Steganalysis determines the presence of secret data by comparing the statistical features of both cover and stego images. Using a large number of features for Steganalysis relative to training set size may reduce classification accuracy and also increase computational complexity. Feature Selection selects subset of features from the given dataset which maximizes classifiers performance. In this paper we have applied four different filter feature selection approaches to features extracted from transform domain and then compared their performance by using five different classifiers.