Residual convolution network based steganalysis with adaptive content suppression

Songtao Wu, Sheng Hua Zhong, Yan Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781509060672
Publication statusPublished - 28 Aug 2017
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Country/TerritoryHong Kong
CityHong Kong


  • Adaptive content suppression
  • Convolutional neural network
  • Image steganalysis
  • Residual learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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