Particle Swarm Optimization Feature Selection 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 cover images or the stego-images. We present a particle swarm optimization algorithm for feature selection for image steganalysis. Experiment results show that the proposed hybrid algorithm for feature selection increases the testing accuracy of classification. The combination of the feature sets extracted is likely to improve the performance of general steganalysis methods which have more practical value for deterring covert communications and the uncorrelated features extracted contain more discriminatory information when distinguish different kinds of steganography.