Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks
Joint Authors
Hu, Donghui
Shen, Qiang
Zhou, Shengnan
Liu, Xueliang
Fan, Yuqi
Wang, Lina
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Hu, Donghui& Shen, Qiang& Zhou, Shengnan& Liu, Xueliang& Fan, Yuqi& Wang, Lina. 2017. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1202808
Modern Language Association (MLA)
Hu, Donghui…[et al.]. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks. Security and Communication Networks No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1202808
American Medical Association (AMA)
Hu, Donghui& Shen, Qiang& Zhou, Shengnan& Liu, Xueliang& Fan, Yuqi& Wang, Lina. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1202808
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202808