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

Abstract

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
Pages4376-4379
Number of pages4
DOIs
StatePublished - Nov 18 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

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Bayram, S., Dirik, A. E., Sencar, H. T., & Memon, N. (2010). An ensemble of classifiers approach to steganalysis. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 4376-4379). [5597874] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.1064