Textural features for steganalysis

Yun Q. Shi, Patchara Sutthiwan, Licong Chen

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

52 Scopus citations


It is observed that the co-occurrence matrix, one kind of textural features proposed by Haralick et al., has played a very critical role in steganalysis. On the other hand, the data hidden in the image texture area has been known difficult to detect for years, and the modern steganographic schemes tend to embed data into complicated texture area where the statistical modeling becomes difficult. Based on these observations, we propose to learn and utilize the textural features from the rich literature in the field of texture classification for further development of the modern steganalysis. As a demonstration, a group of textural features, including the local binary patterns, Markov neighborhoods and cliques, and Laws' masks, have been selected to form a new set of 22,153 features, which are used with the FLD-based ensemble classifier to steganalyze the HUGO on BOSSbase 0.92. At the embedding rate of 0.4 bpp (bit per pixel) an average detection accuracy of 83.92% has been achieved. It is expected that this new approach can enhance our capability in steganalysis.

Original languageEnglish (US)
Title of host publicationInformation Hiding - 14th International Conference, IH 2012, Revised Selected Papers
Number of pages15
StatePublished - 2013
Event14th International Conference on Information Hiding, IH 2012 - Berkeley, CA, United States
Duration: May 15 2012May 18 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Information Hiding, IH 2012
Country/TerritoryUnited States
CityBerkeley, CA

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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