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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 20 — Jul. 10, 2012
  • pp: 4667–4677

Digital video steganalysis using motion vector recovery-based features

Yu Deng, Yunjie Wu, and Linna Zhou  »View Author Affiliations

Applied Optics, Vol. 51, Issue 20, pp. 4667-4677 (2012)

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As a novel digital video steganography, the motion vector (MV)-based steganographic algorithm leverages the MVs as the information carriers to hide the secret messages. The existing steganalyzers based on the statistical characteristics of the spatial/frequency coefficients of the video frames cannot attack the MV-based steganography. In order to detect the presence of information hidden in the MVs of video streams, we design a novel MV recovery algorithm and propose the calibration distance histogram-based statistical features for steganalysis. The support vector machine (SVM) is trained with the proposed features and used as the steganalyzer. Experimental results demonstrate that the proposed steganalyzer can effectively detect the presence of hidden messages and outperform others by the significant improvements in detection accuracy even with low embedding rates.

© 2012 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(150.1135) Machine vision : Algorithms
(110.4155) Imaging systems : Multiframe image processing
(100.4992) Image processing : Pattern, nonlinear correlators

ToC Category:
Image Processing

Original Manuscript: January 24, 2012
Revised Manuscript: April 12, 2012
Manuscript Accepted: May 14, 2012
Published: July 3, 2012

Yu Deng, Yunjie Wu, and Linna Zhou, "Digital video steganalysis using motion vector recovery-based features," Appl. Opt. 51, 4667-4677 (2012)

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