Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we c...

Research in steganalysis is motivated by the concern that communications associated with illicit activity could be hidden in seemingly innocent electronic transactions. By developing defensive tools before steganographic communication grows, computer security professionals will be better prepared for the threat. This paper proposes a computational immune system (CIS) approach to blind steganogr...

Steganography is the art of secret communication between two parties that not only conceals the contents of a message, but also its existence. Steganalysis attempts to detect the existence of embedded data in a steganographically altered cover file. Many algorithms have been proposed, but so far each has had some weakness that has allowed its effects to be detected, usually through first or sec...

This paper presents a new blind approach of image Steganalysis based on contourlet transform and non linear support vector machine. Properties of Contourlet transform are used to extract features of images, and non linear support vector machine is used to classify the stego and cover images. The important aspect of this paper is that, it uses the minimum number of features in the transform doma...

Translation based steganography (TBS) is a kind of relatively new and secure linguistic steganography proposed. It takes advantage of the “noise” created by automatic translation of natural language text to encode the secret information. Up to date, there is little research on the steganalysis against this kind of linguistic steganography. In this paper, a blind steganalytic method, which is na...

The contribution of this paper is two-fold. First, we describe an improved version of a blind steganalysis method previously proposed by Holotyak et al. and compare it to current state-of-the-art blind steganalyzers. The features for the blind classifier are calculated in the wavelet domain as higher-order absolute moments of the noise residual. This method clearly shows the benefit of calculat...

In this paper, we construct blind steganalyzers for JPEG images capable of assigning stego images to known steganographic programs. Each JPEG image is characterized using 23 calibrated features calculated from the luminance component of the JPEG file. Most of these features are calculated directly from the quantized DCT coefficients as their first order and higher-order statistics. The features...

This paper considers the problem of blind active spreadspectrum (SS) steganalysis defined as the extraction of hidden data with no prior information. We first develop a multisignature iterative generalized least-squares (M-IGLS) core procedure to seek unknown messages hidden in image hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding...

Blind steganalysis is based on choice of the feature set and the machine learning classifiers used for classification. While the performance of individual classifiers is good, the classification accuracy is seen to increase by appropriate combination of classifiers. This research has implemented image steganalysis with fusion of classifiers by various data fusion schemes. We intend to analyse t...