Steganalysis based on awareness of selection-channel and deep learning

Jianhua Yang, Kai Liu, Xiangui Kang, Edward Wong, Yunqing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Recently, deep learning has been used in steganalysis based on convolutional neural networks (CNN). In this work, we propose a CNN architecture (the so-called maxCNN) to use the selection channel. It is the first time that the knowledge of the selection channel has been incorporated into CNN for steganalysis. The proposed method assigns large weights to features learned from complex texture regions while assigns small weights to features learned from smooth regions. Experimental results on the well-known dataset BOSS-base have demonstrated that the proposed scheme is able to improve detection performance, especially for low embedding payloads. The results have shown that with the ensemble of maxCNN and maxSRMd2+EC, the proposed method can obtain better performance compared with the reported state-of-the-art on detecting WOW embedding algorithm.

Original languageEnglish (US)
Title of host publicationDigital Forensics and Watermarking - 16th International Workshop, IWDW 2017, Proceedings
EditorsYun-Qing Shi, Hyoung Joong Kim, Christian Kraetzer, Jana Dittmann
PublisherSpringer Verlag
Pages263-272
Number of pages10
ISBN (Print)9783319641843
DOIs
StatePublished - 2017
Event16th International Workshop on Digital Forensics and Watermarking, IWDW 2017 - Magdeburg, Germany
Duration: Aug 23 2017Aug 25 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Workshop on Digital Forensics and Watermarking, IWDW 2017
CountryGermany
CityMagdeburg
Period8/23/178/25/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Adaptive steganography
  • Convolutional neural networks (CNN)
  • Selection-channel
  • Steganalysis

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