Cover-Source Mismatch in Deep Spatial Steganalysis

  • Xunpeng Zhang
  • Xiangwei KongEmail author
  • Pengda Wang
  • Bo Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12022)


In conventional image steganalysis, cover-source mismatch is a serious problem restricting its utility. In our work, we validate that in deep steganalysis, cover-source mismatch still exists. But unlike in conventional scenarios, sharp accuracy reduction just exists in a part of cover-source mismatch scenarios in deep steganalysis. To explain this phenomenon, we use A-distance to measure the texture complexity between databases. Furthermore, to ease the accuracy reduction caused by the mismatch, we adapt JMMD into deep steganalysis and design a new network (J-Net). Extensive experiments prove A-distance and J-Net works well.


Steganalysis Deep learning Cover-source mismatch 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Zhejiang UniversityHangzhouChina

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