Blind image steganalysis using neural networks and wrapper feature selection

Abstract

This paper highlights the importance of wrapper feature selection technique for image steganalysis. Two major processes in blind image steganalysis are feature extraction and classification. To increase the efficiency of classifier feature space has been increased causing “curse of dimensionality”. This leads to requirement of feature selection for steganalysis. This paper discusses the importance of heuristic wrapper approach which is an improved version of Particle Swarm Optimization. The fitness function and classification is based on artificial neural network. The neural networks are fast learning and non-linear classifier which gives it an edge over other classifiers. The experiment results show that feature selection technique improves the classification accuracy with reduced number of feature set and provides better understanding of features.