JPEG Image Steganalysis Using Multivariate PDF Estimates With MRF Cliques

Gokhan Gul, Fatih Kurugollu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Blind steganalysis of JPEG images is addressed by modeling the correlations among the DCT coefficients using K -variate (K = 2) p.d.f. estimates (p.d.f.s) constructed by means of Markov random field (MRF) cliques. The reasoning of using high variate p.d.f.s together with MRF cliques for image steganalysis is explained via a classical detection problem. Although our approach has many improvements over the current state-of-the-art, it suffers from the high dimensionality and the sparseness of the high variate p.d.f.s. The dimensionality problem as well as the sparseness problem are solved heuristically by means of dimensionality reduction and feature selection algorithms. The detection accuracy of the proposed method(s) is evaluated over Memon's (30.000 images) and Goljan's (1912 images) image sets. It is shown that practically applicable steganalysis systems are possible with a suitable dimensionality reduction technique and these systems can provide, in general, improved detection accuracy over the current state-of-the-art. Experimental results also justify this assertion.
Original languageEnglish
Article number6461936
Pages (from-to)578-587
Number of pages10
JournalIEEE Transactions on Information Forensics and Security
Issue number3
Publication statusPublished - Mar 2013


  • Steganalysis
  • Steganography
  • Classification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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