On optimal feature selection using intelligent optimization methods for image steganalysis

The purpose of image steganalysis is to detect the presence of hidden messages in cover images. Steganalysis can be considered as a pattern recognition process to decide which class a test image belongs to: the innocent photographic image or the stego-image. We compare harmony search algorithm and particle swarm optimization algorithm based feature selection for image steganalysis. Experiment results show that the proposed hybrid algorithm for feature selection is capable of increasing the testing accuracy of classifying result. The combination of the feature sets extracted with the proposed method is feasible to improve the performance of general steganalysis in a reduced dimension. Experiment results also show that this method has the potential to distinguish different kinds of steganography with the extracted uncorrelated features which contain more discriminatory information. © 2013 Binary Information Press.