Nonparametric steganalysis of QIM data hiding using approximate entropy

Hafiz Malik, K. P. Subbalakshmi, R. Chandramouli

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

16 Scopus citations

Abstract

This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the cover- and the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas message embedding using QIM increases irregularity in the resulting QIM-stego image. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test-image. Simulation results presented in this paper show that the proposed steganalysis technique can distinguish between the cover- and the stego-image with low false rates (i.e. Pfp < 0.1 & Pfn < 0.07 for dither modulation stego and Pfp < 0.12 & Pfn < 0.002 for QIM-stego).

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Security, Forensics, Steganography, and Watermarking of Multimedia Contents X
DOIs
StatePublished - 2008
EventSecurity, Forensics, Steganography, and Watermarking of Multimedia Contents X - San Jose, CA, United States
Duration: 28 Jan 200830 Jan 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6819
ISSN (Print)0277-786X

Conference

ConferenceSecurity, Forensics, Steganography, and Watermarking of Multimedia Contents X
Country/TerritoryUnited States
CitySan Jose, CA
Period28/01/0830/01/08

Keywords

  • Algorithmic entropy
  • Approximate entropy
  • Complexity
  • Dither modulation
  • Entropy
  • Quantization index modulation
  • Steganalysis
  • Steganography

Fingerprint

Dive into the research topics of 'Nonparametric steganalysis of QIM data hiding using approximate entropy'. Together they form a unique fingerprint.

Cite this