Improving steganalysis by fusion techniques: A case study with image steganography

Mehdi Kharrazi, Husrev T. Senear, Nasir Memon

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

Abstract

In the past few years, we have witnessed a number of powerful steganalysis technique proposed in the literature. These techniques could be categorized as either specific or universal. Each category of techniques has a set of advantages and disadvantages. A steganalysis technique specific to a steganographic embedding technique would perform well when tested only on that method and might fail on all others. On the other hand, universal steganalysis methods perform less accurately overall but provide acceptable performance in many cases. In practice, since the steganalyst will not be able to know what steganographic technique is used, it has to deploy a number of techniques on suspected images. In such a setting the most important question that needs to be answered is: What should the steganalyst do when the decisions produced by different steganalysis techniques are in contradiction? In this work, we propose and investigate the use of information fusion methods to aggregate the outputs of multiple steganalysis techniques. We consider several fusion rules that are applicable to steganalysis, and illustrate, through a number of case studies, how composite steganalyzers with improved performance can be designed. It is shown that fusion techniques increase detection accuracy and offer scalability, by enabling seamless integration of new steganalysis techniques.

Original languageEnglish (US)
Title of host publicationTransactions on Data Hiding and Multimedia Security I
Pages123-137
Number of pages15
Volume4300 LNCS
StatePublished - 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4300 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Fingerprint

Steganalysis
Steganography
Information fusion
Scalability
Fusion
Composite materials
Fusion Rule
Information Fusion
Composite

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kharrazi, M., Senear, H. T., & Memon, N. (2006). Improving steganalysis by fusion techniques: A case study with image steganography. In Transactions on Data Hiding and Multimedia Security I (Vol. 4300 LNCS, pp. 123-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4300 LNCS).

Improving steganalysis by fusion techniques : A case study with image steganography. / Kharrazi, Mehdi; Senear, Husrev T.; Memon, Nasir.

Transactions on Data Hiding and Multimedia Security I. Vol. 4300 LNCS 2006. p. 123-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4300 LNCS).

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

Kharrazi, M, Senear, HT & Memon, N 2006, Improving steganalysis by fusion techniques: A case study with image steganography. in Transactions on Data Hiding and Multimedia Security I. vol. 4300 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4300 LNCS, pp. 123-137.
Kharrazi M, Senear HT, Memon N. Improving steganalysis by fusion techniques: A case study with image steganography. In Transactions on Data Hiding and Multimedia Security I. Vol. 4300 LNCS. 2006. p. 123-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kharrazi, Mehdi ; Senear, Husrev T. ; Memon, Nasir. / Improving steganalysis by fusion techniques : A case study with image steganography. Transactions on Data Hiding and Multimedia Security I. Vol. 4300 LNCS 2006. pp. 123-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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