Blind image steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system

This paper reports the design principles and evaluation results of a new experimental universal, blind image steganalysing system. This system investigates the use of content independent statistical evidences left by the steganograms, as features for an image steganalyzer. The work is aimed at maximizing the sensitivity and specificity of the steganalyzer and to accomplish both security and system performance. A genetic-X-means classifier is constructed to realize the proposed model. For performance evaluation, a database composed of 5600 plain and stego images (generated by using seven different embedding schemes) was established. The results of our empirical experiment prove the vitality of the proposed scheme in detecting stego anomalies in images. In addition, the simulation results show that the effectiveness of steganalytic system can be enhanced by considering the content independent distortion measures and maximizing the sensitivity and specificity of the system.