Steganalysis of QIM-based data hiding using kernel density estimation

Hafiz Malik, K. P. Subbalakshmi, Rajarathnam Chandramouli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

This paper presents a novel steganalysis technique to attack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embedding disturbs neighborhood correlation in the transform domain. We estimate the probability density function (pdf) of this statistical change in a systematic manner using a kernel density estimate (KDE) method. The estimated parametric density model is then used for stego message detection. The impact of the choice of kernels on the estimated density is investigated experimentally. Simulation results evaluated on a large dataset of 6000 quantized images indicate that the proposed method is reliable. The impact of the choice of message embedding parameters on the accuracy of the steganalysis detection is also evaluated. Simulation results show that the proposed method can distinguish between the quantized-cover and the QIM-stego with low false alarm rates (i.e. Pfn0.03 and Pfp0.19). We demonstrate that the proposed steganalysis scheme can successfully attack steganographic tools like Jsteg and JP Hide and Seek as well.

Original languageEnglish
Title of host publicationMM and Sec'07 - Proceedings of the Multimedia and Security Workshop 2007
Pages149-160
Number of pages12
DOIs
StatePublished - 2007
EventMM and Sec'07 - 9th Multimedia and Security Workshop 2007 - Dallas, TX, United States
Duration: 20 Sep 200721 Sep 2007

Publication series

NameMM and Sec'07 - Proceedings of the Multimedia and Security Workshop 2007

Conference

ConferenceMM and Sec'07 - 9th Multimedia and Security Workshop 2007
Country/TerritoryUnited States
CityDallas, TX
Period20/09/0721/09/07

Keywords

  • Gamma density
  • Kernel density estimation
  • Mode
  • Quantization index modulation
  • Skewness
  • Steganalysis
  • Steganography

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