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Security and Communication Networks
Volume 2017 (2017), Article ID 2314860, 9 pages
Research Article

Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

1School of Computer and Information, Hefei University of Technology, Hefei 230009, China
2School of Computer and Information, Wuhan University, Wuhan 430072, China

Correspondence should be addressed to Donghui Hu

Received 19 September 2017; Accepted 23 October 2017; Published 12 November 2017

Academic Editor: Zhenxing Qian

Copyright © 2017 Donghui Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.