A cost-effective decision tree based approach to steganalysis

Liyun Li, Husrev Taha Sencar, Nasir Memon

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


An important issue concerning real-world deployment of steganalysis systems is the computational cost of acquiring features used in building steganalyzers. Conventional approach to steganalyzer design crucially assumes that all features required for steganalysis have to be computed in advance. However, as the number of features used by typical steganalyzers grow into thousands and timing constraints are imposed on how fast a decision has to be made, this approach becomes impractical. To address this problem, we focus on machine learning aspect of steganalyzer design and introduce a decision tree based approach to steganalysis. The proposed steganalyzer system can minimize the average computational cost for making a steganalysis decision while still maintaining the detection accuracy. To demonstrate the potential of this approach, a series of experiments are performed on well known steganography and steganalysis techniques.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Media Watermarking, Security, and Forensics 2013
StatePublished - 2013
Event2013 Media Watermarking, Security, and Forensics Conference - Burlingame, CA, United States
Duration: Feb 5 2013Feb 7 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


Other2013 Media Watermarking, Security, and Forensics Conference
Country/TerritoryUnited States
CityBurlingame, CA


  • Computational cost effective
  • Decision tree
  • Steganography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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