Article

Video steganalysis using motion estimation

Authors:

K. Kancherla,

S. MukkamalaAuthors Info & Claims

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

Pages 3129 - 3134

Published: 14 June 2009 Publication History

Abstract

In this paper we present a novel video steganalysis method using neural networks and support vector machines to detect video steganograms with very limited a-prior knowledge about the steganogram embedding method.

We apply temporal and spacial redundancies by using the concept of motion estimation widely used in video compression to every frame to obtain an estimate of the frame and extract the merged Discrete Cosine Features (DCT) and markov features. MSU stegovideo tool by Moscow State University and the spread spectrum steganography tool are used for producing video steganograms.

Results show that the features we use give the best accuracy to detect video steganograms. Our results thus demonstrate the potential of using learning machines and motion estimation in detecting video steganograms.

References

[1]

U. Budhia, D. Kundur and T. Zourntos, "Digital Video Steganalysis Exploiting Statistical Visibility in the Temporal Domain," IEEE Transactions on Information Forensics and Security, vol. 1, no. 1, pp. 43-55, March 2006.

Digital Library

Google Scholar

[2]

U. Budhia and D. Kundur, "Digital Video Steganalysis Exploiting Collusion Sensitivity," "Sensors, Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense," Edward M. Carapezza, ed., Proc. SPIE (vol. 5403), April 2004.

Google Scholar

[3]

H. Zhao, M. Wu, Z. Wang, and KJ.R. Liu, "Forensic Analysis of Nonlinear Collusion Attacks for Multimedia Fingerprinting," IEEE Transactions on Image Processing, vol. 14, no. 5, pp. 646-661, May 2005.

Digital Library

Google Scholar

[4]

MSU Stegovideo http://compression.ru/video/stego_video/index en.html

Google Scholar

[5]

J. Cox, J. Kilian, T. Leighton, T. Shamoon, "Secure Spread Spectrum Watermarking for Multimedia," IEEE Transactions on Image Processing, vol. 6, no. 12, pp. 1673-1687, December 1997.

Digital Library

Google Scholar

[6]

T. Pevny, J. Fridrich, "Merging Markov and DCT features for multi-class JPEG steganalysis," Proceedings of SPIE Electronic Imaging, Photonics West, pp. 03-04, January 2007.

Google Scholar

[7]

V. Cherkassy, "Model complexity control and statistical learning theory," Journal of Natural Computing, Vol. 1, pp 109-133, 2002.

Digital Library

Google Scholar

[8]

J. H. Lee, C. J. Lin, "Automatic model selection for support vector machines," Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, 2000.

Google Scholar

[9]

C. C. Chang, C. J. Lin, "LIBSYM: a library for support vector machines," Department of Computer Science and Information Engineering, National Taiwan University, 2001.

Google Scholar

[10]

A. F. Moller, "A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning," Neural Networks, Vol. 6, pp. 525-533, 1993.

Digital Library

Google Scholar

[11]

Egan, J.P: Signal detection theory and ROC analysis. New York: Academic Press, (1975).

Google Scholar

Cited By

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Wang KHan JWang H(2014)Digital video steganalysis by subtractive prediction error adjacency matrixMultimedia Tools and Applications10.1007/s11042-013-1373-472:1(313-330)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s11042-013-1373-4
Ker ABas PBöhme RCogranne RCraver SFiller TFridrich JPevný TPuech WChaumont MDittmann JCampisi P(2013)Moving steganography and steganalysis from the laboratory into the real worldProceedings of the first ACM workshop on Information hiding and multimedia security10.1145/2482513.2482965(45-58)Online publication date: 17-Jun-2013
https://dl.acm.org/doi/10.1145/2482513.2482965
DeVito MHofmeister CJochen MSchembari NWhitman MZafar H(2011)Undergraduate research in computer forensicsProceedings of the 2011 Information Security Curriculum Development Conference10.1145/2047456.2047466(61-68)Online publication date: 30-Sep-2011
https://dl.acm.org/doi/10.1145/2047456.2047466

Video steganalysis using motion estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Animation

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IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

June 2009

3570 pages

ISBN:9781424435494

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IEEE Press

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Published: 14 June 2009

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Wang KHan JWang H(2014)Digital video steganalysis by subtractive prediction error adjacency matrixMultimedia Tools and Applications10.1007/s11042-013-1373-472:1(313-330)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s11042-013-1373-4
Ker ABas PBöhme RCogranne RCraver SFiller TFridrich JPevný TPuech WChaumont MDittmann JCampisi P(2013)Moving steganography and steganalysis from the laboratory into the real worldProceedings of the first ACM workshop on Information hiding and multimedia security10.1145/2482513.2482965(45-58)Online publication date: 17-Jun-2013
https://dl.acm.org/doi/10.1145/2482513.2482965
DeVito MHofmeister CJochen MSchembari NWhitman MZafar H(2011)Undergraduate research in computer forensicsProceedings of the 2011 Information Security Curriculum Development Conference10.1145/2047456.2047466(61-68)Online publication date: 30-Sep-2011
https://dl.acm.org/doi/10.1145/2047456.2047466

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