An ensemble of classifiers approach to steganalysis

S. Bayram, A. E. Dirik, H. T. Sencar, N. Memon

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


Most work on steganalysis, except a few exceptions, have primarily focused on providing features with high discrimination power without giving due consideration to issues concerning practical deployment of steganalysis methods. In this work, we focus on machine learning aspect of steganalyzer design and utilize a hierarchical ensemble of classifiers based approach to tackle two main issues. Firstly, proposed approach provides a workable and systematic procedure to incorporate several steganalyzers together in a composite steganalyzer to improve detection performance in a scalable and cost-effective manner. Secondly, since the approach can be readily extended to multi-class classification it can also be used to infer the steganographic technique deployed in generation of a stego-object. We provide results to demonstrate the potential of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Number of pages4
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other2010 20th International Conference on Pattern Recognition, ICPR 2010

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'An ensemble of classifiers approach to steganalysis'. Together they form a unique fingerprint.

Cite this