Block dependency feature based classification scheme for uncalibrated image steganalysis

Abstract

Steganalysis is a technique of detecting hidden information sent over a communication medium. In this paper, we present a powerful new blind steganalytic scheme that can reliably detect hidden data in JPEG images. This would increase the success rate of steganalysis by detecting data in transform domain. This scheme is feature based in the sense that features that are sensitive to embedding changes and being employed as means of steganalysis. The features are extracted in DCT domain. DCT domain features have extended DCT features and Markovian features merged together to eliminate the drawbacks of both. The blind steganalytic technique has a broad spectrum of analyzing different embedding techniques. The feature based steganalytic technique is used in the DCT domain to extract about 23 functionals and classify the dataset according to these functionals. The feature set can be increased to about 274 features by merging both DCT and Markovian features. The extracted features are being fed to a classifier which helps to distinguish between a cover and stego image. The classification is also done with inter block dependency features and intra block dependency features within the 274 features. Support Vector Machine is used as classifier here. © 2012 Springer-Verlag.