Abstract
Steganography is the science of hiding information in some other medium. These media can be text, images, audio or video files. Steganographic analysis (steganalysis), on the other hand, is the science of detecting the existence of hidden information.
Many steganalysis methods have been introduced in the literature. These methods have been developed to combat specific steganography techniques and to detect data hidden in specific image formats. However, no single steganalysis method or tool can detect all types of steganography or support all available image formats.
This paper presented an image steganalysis system that combines the colour gradient co-occurrence matrix (CGCM) features and number of histogram features. The proposed system is considered as blind image steganalysis, which rely on the extraction of selections of image features.
The CGCM takes into account information of both colour correlations and gradients among the pixels in an image.
In addition, the histogram features are extracted by exploiting the histogram of difference image, which is usually a generalised Gaussian distribution centered at 0.
The tested CGCM features and histogram features were merged together to improve the performance of the system. Merging two different types of features allows taking advantages of the beneficial properties of each in order to increase the system ability in terms of detection.
The proposed detection system was trained and tested to distinguish stego images from clean ones using the iscriminant Analysis (DA) classification method and Multilayer Perceptron neural network (MLP).
The experimental results prove that the proposed system possesses reliable detection ability and accuracy. The proposed system is a more generalized detector than previous systems, covering a wider variety of types of stego images and image formats.