Deep Convolutional Network for Steganalysis of HUGO, WOW, and UNIWARD algorithms

This paper presents a novel convolutional neural network (CNN) architecture for image steganalysis. We trained the CNN to detect distortions introduced by the state-of-the-art embedding algorithms: WOW, HUGO, and UNIWARD. We used a large database of test images and a large number of stegomessages, which resulted in a six billion-sample training set. The experimental results show that the resultant CNN, which was trained on such a large dataset, outperforms state-of-the-art steganalysis algorithms in terms of total error rate.