Advertisement

Video Steganography with Perturbed Motion Estimation

  • Yun Cao
  • Xianfeng Zhao
  • Dengguo Feng
  • Rennong Sheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

In this paper, we propose an adaptive video steganography tightly bound to video compression. Unlike traditional approaches utilizing spatial/transformed domain of images or raw videos which are vulnerable to certain existing steganalyzers, our approach targets the internal dynamics of video compression. Inspired by Fridrich et al’s perturbed quantization (PQ) steganography, a technique called perturbed motion estimation (PME) is introduced to perform motion estimation and message hiding in one step. Intending to minimize the embedding impacts, the perturbations are optimized with the hope that these perturbations will be confused with normal estimation deviations. Experimental results show that, satisfactory levels of visual quality and security are achieved with adequate payloads.

Keywords

Mean Square Error Motion Estimation Visual Quality Data Hiding Video Compression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Xvid Codec 1.1.3 (2009), http://www.xvid.org/
  2. 2.
    Aly, H.: Data hiding in motion vectors of compressed video based on their associated prediction error. IEEE Transactions on Information Forensics and Security 6(1), 14–18 (2011)CrossRefGoogle Scholar
  3. 3.
    Bellifemine, F., Capellino, A., Chimienti, A., Picco, R., Ponti, R.: Statistical analysis of the 2d-dct coefficients of the differential signal for images. Signal Processing: Image Communication 4(6), 477–488 (1992)Google Scholar
  4. 4.
    Budhia, U., Kundur, D., Zourntos, T.: Digital video steganalysis exploiting statistical visibility in the temporal domain. IEEE Transactions on Information Forensics and Security 1(4), 502–516 (2006)CrossRefGoogle Scholar
  5. 5.
    Cachin, C.: An information-theoretic model for steganography. Cryptology ePrint Archive, Report 2000/028 (2000)Google Scholar
  6. 6.
    Chang, C., Lin, C.: Libsvm: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  7. 7.
    Fang, D.Y., Chang, L.W.: Data hiding for digital video with phase of motion vector. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1422–1425 (2006)Google Scholar
  8. 8.
    Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography with wet paper codes. In: Proceedings of the 2004 Workshop on Multimedia & Security, MM&Sec 2004, pp. 4–15. ACM, New York (2004)Google Scholar
  9. 9.
    Gormish, M.J., Gill, J.T.: Computation-rate-distortion in transform coders for image compression. In: SPIE Visual Communications and Image Processing, pp. 146–152 (1993)Google Scholar
  10. 10.
    Jainsky, J.S., Kundur, D., Halverson, D.R.: Towards digital video steganalysis using asymptotic memoryless detection. In: Proceedings of the 9th Workshop on Multimedia & Security, MM&Sec 2007, pp. 161–168. ACM, New York (2007)Google Scholar
  11. 11.
    Kutter, M., Jordan, F., Ebrahimi, T.: Proposal of a watermarking technique for hiding/retrieving data in compressed and decompressed video. Technical report M2281, ISO/IEC document, JTC1/SC29/WG11 (1997)Google Scholar
  12. 12.
    Pankajakshan, V., Doerr, G., Bora, P.K.: Detection of motion-incoherent components in video streams. IEEE Transactions on Information Forensics and Security 4(1), 49–58 (2009)CrossRefGoogle Scholar
  13. 13.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on Information Forensics and Security 5(2), 215–224 (2010)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  15. 15.
    Xu, C., Ping, X., Zhang, T.: Steganography in compressed video stream. In: Proceedings of the First International Conference on Innovative Computing, Information and Control, ICICIC 2006, pp. 269–272. IEEE Computer Society, Washington, DC, USA (2006)Google Scholar
  16. 16.
    Xuan, G., Shi, Y.Q., Gao, J., Zou, D., Yang, C., Zhang, Z., Chai, P., Chen, C., Chen, W.: Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: Barni, M., Herrera-Joancomartí, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 262–277. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Zhang, C., Su, Y.: Video steganalysis based on aliasing detection. Electronics Letters 44(13), 801–803 (2008)CrossRefGoogle Scholar
  18. 18.
    Zhang, C., Su, Y., Zhang, C.: A new video steganalysis algorithm against motion vector steganography. In: Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yun Cao
    • 1
    • 2
  • Xianfeng Zhao
    • 1
  • Dengguo Feng
    • 1
  • Rennong Sheng
    • 3
  1. 1.State Key Laboratory of Information SecurityInstitute of Software, Chinese Academy of SciencesBeijingP.R. China
  2. 2.Graduate University of Chinese Academy of SciencesBeijingP.R. China
  3. 3.Beijing Institute of Electronics Technology and ApplicationBeijingP.R. China

Personalised recommendations