Abstract
Various 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.
One of the problems is the need for a more general system to cover different types of image formats and the ability to detect a wider range of stego images, as blindly created by many steganography methods.
This paper has presented an image steganalysis system to distinguished between clean and stego images using three different techniques.
The first technique is the extraction of a large number of image features from the colour gradient co-occurrence matrix (CGCM).
The second is the extraction of a number of histogram features by exploiting the histogram of difference image, which is usually a generalised Gaussian distribution centred at 0.
Finally, the CGCM features and histogram features tested were merged to improve the performance of the system. Merging two different types of features allows one to take advantage of the beneficial properties of each in order to increase system ability in terms of detection.
The experimental results demonstrate 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 stego image types and image formats.
In addition, experimental results show that the proposed steganalysis system performed considerably better than some previous detection methods.