Please use this identifier to cite or link to this item:
Title: Residual convolution network based steganalysis with adaptive content suppression
Authors: Wu, S 
Zhong, SH
Liu, Y 
Keywords: Adaptive content suppression
Convolutional neural network
Image steganalysis
Residual learning
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings - IEEE International Conference on Multimedia and Expo, 2017, 8019304, p. 241-246 How to cite?
Abstract: Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. In this paper, we propose a unified Convolutional Neural Network (CNN) model for this task. In order to reliably detect modern steganographic algorithms, we design the proposed model from two aspects. For the first, different from existing CNN based steganalytic algorithms that use a predefined highpass kernel to suppress image content, we integrate the highpass filtering operation into the proposed network by building a content suppression subnetwork. For the second, we propose a novel sub-network to actively preserve the weak stego signal generated by secret messages based on residual learning, making the successive network capture the difference between cover images and stego images. Extensive experiments demonstrate that the proposed model can detect states-of-the-art steganography with much lower detection error rates than previous methods.
ISBN: 9781509060672
ISSN: 1945-7871
DOI: 10.1109/ICME.2017.8019304
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Mar 29, 2019

Page view(s)

Last Week
Last month
Citations as of May 21, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.