Unconditional Steganalysis of JPEG and BMP Images and Its Performance Analysis Using Support Vector Machine

Abstract

A feature based steganalytic method used for detecting both transform and spatial domain embedding techniques was developed. We developed an unconditional steganalysis which will automatically classify an image as having hidden information or not using a powerful classifier Support Vector Machine which is independent of any embedding techniques. To select the most relevant features from the total 269 features extracted, they apply Principal Component Analysis. Experimental results showed that our steganalysis scheme blindly detect the images obtained from six steganographic algorithms- F5, Outguess, S-Tool, JP Hide & Seek, LSB flipping and PVD. This method is able to detect any new algorithms which are not used during the training step, even if the embedding rate is very low. We also analyzed embedding rate versus detectability performances. © Springer-Verlag Berlin Heidelberg 2010.