Advertisement

SVD-Based Approach to Transparent Embedding Data into Digital Images

  • Vladimir I. Gorodetski
  • Leonard J. Popyack
  • Vladimir Samoilov
  • Victor A. Skormin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2052)

Abstract

A new approach to transparent embedding of data into digital images is proposed. It provides a high rate of the embedded data and is robust to common and some intentional distortions. The developed technique employs properties of the singular value decomposition (SVD) of a digital image. According to these properties each singular value (SV) specifies the luminance of the SVD image layer, whereas the respective pair of singular vectors specifies image geometry. Therefore slight variations of SVs cannot affect the visual perception of the cover image. The proposed approach is based on embedding a bit of data through slight modifications of SVs of a small block of the segmented covers. The approach is robust because it supposes to embed extra data into low bands of covers in a distributed way. The size of small blocks is used as an attribute to achieve a tradeoff between the embedded data rate and robustness. An advantage of the approach is that it is blind. Simulation has proved its robustness to JPEG up to 40%. The approach can be used both for hidden communication and watermarking.

Keywords

Singular Value Decomposition Cover Image Singular Vector JPEG Compression Hide Data 
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.
    Anderson, R., Petitcolas, F.A.P.: On the Limits of Steganography. In: IEEE Journal of Selected Areas of Communications, Vol. 16(4) (1998) 474–481CrossRefGoogle Scholar
  2. 2.
    Andrews, H.C., Patterson, C.L.: Singular Value Decomposition (SVD) for Image Coding. In: IEEE Transaction on Communication. Vol. 24 (1976) 425–432CrossRefGoogle Scholar
  3. 3.
    Bender, W., Gruhl, D., Morimoto, N., and Lu, A.: Techniques for Data Hiding. In: IBM System Journal, Vol. 35(3&4) (1996)Google Scholar
  4. 4.
    Bruyndonckx, O., Quisquater, J.J., and Macq, B.: Spatial Method for Copyright Labeling of Digital Images. In: Proceedings of IEEE Workshop on nonlinear signal and image Processing, Greece (1995)Google Scholar
  5. 5.
    Burget, S., Koch, E., and Zhao, J.: A Novel Method for Copyright Labeling Digitized Image Data. Technical Report, Fraunhofer Institute for Computer Graphics, Germany (1994)Google Scholar
  6. 6.
    Chen B., and Wornell, G.W.: Dither Modulation: A new Approach to Digital Watermarking and Information Embedding. In: Ping Wah Wong and E.J. Delp (eds). Vol. 3657, San Jose, CA, USA (1999)Google Scholar
  7. 7.
    Cox, I.J. and Miller, M.: A Review on Watermarking and the Importance of Perceptual Modeling. In: Proceedings of the Conference on Electronic Imaging (1997)Google Scholar
  8. 8.
    Fridrich, J., Goljan, M.: Protection of Digital Images using self-embedding. In: Proceedings of the Second International Scientific Conference in the Republic of Kaazakhstan “Information Technologies and Control”, Almaty, Kazakhstan (1999) 302–311Google Scholar
  9. 9.
    Fukutomi, T., Tahara, O., Okamoto, N., Minami, T.: Encoding of Still Pictures by a Wavelet Transform and Singular Value Decomposition. In: Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering. Alberta (1999) 18–23Google Scholar
  10. 10.
    Gorodetski, V., Skormin, V., Popyack, L.: Singular Value Decomposition Approach to Digi-tal Image Lossy Compression. In: Proceedings of the 4-th World Conference “Systems, Cybernetics and Informatics-2000” (SCI-2000), Orlando, USA (2000)Google Scholar
  11. 11.
    Horn, R.A. and Johnson, C.R.: Matrix Analysis. Cambridge University Press (1988)Google Scholar
  12. 12.
    Johnson, N., Jagodia, S.: Exploring Steganography: Seeing the Unseen. Computer, February (1998) 26–34Google Scholar
  13. 13.
    Johnson, N., Duric, Z., Jajodia, S.: Information Hiding. Steganography and Watermarking-Attacks and Countermeasures. Kluwer Academic Pub. (2000)Google Scholar
  14. 14.
    Katzenbeisser, S., Petitcolas F.A.P. (eds): Information Hiding Techniques for Staganography and Digital Watermarking. Artech House Books (2000)Google Scholar
  15. 15.
    Kundur, D. and Hatzinakos, D.: A robust Digital Watermarking Method using Waveletbased Fusion. In: Proceedings of the International Conference on Image Processing, Vol. 1, USA, IEEE (1997)Google Scholar
  16. 16.
    Machado, R.: EZ Stego. http://www.stego.com.
  17. 17.
    Matsui, K. and Tanaka, K.: Video Steganography: How to Embed a Signature in a Picture. In: Proceedings of IMA Intellectual property, Vol. 1(1) (1999) 187–206.Google Scholar
  18. 18.
    Petitcolas, F.A.P., Anderson, R.J. and Kuhn, M.: Information Hiding-A Survey. In: Proceedings of the IEEE, Special Issue on Protection of Multimedia Content, Vol. 87(7) (1999) 1062–1078Google Scholar
  19. 19.
    Pitas, I.: A method for signature casting on digital images. In: Proceedings of the International Conference on Image Processing (ICIP’96) (1996)Google Scholar
  20. 20.
    Piva, A, Barni, M., Bartoloni, E., and Cappellini, V.: DCT-based Watermarking Recovering without Restoring to the Uncorrupted Original Image In: Proceedings of the International Conference on Image Processing (ICIP), Vol. 1, IEEE (1997)Google Scholar
  21. 21.
    Podilchuck, C.I. and Zeng, W.: Perceptual Watermarking of Still Images. In: Proceedings of the Workshop on Multimedia Signal Processing, Princeton, NJ, USA (1997)Google Scholar
  22. 22.
    Puate J. and Jordan F.: Using Fractal Compression Scheme to Embed a Digital Signature into an Image. In: Proceedings of SPIE, Video Techniques and Software for Full-Service Network, Vol. 2915, Boston, MA, USA (1996) 108–118Google Scholar
  23. 23.
    Smith, J.R. and Comiskey, B.O.: Modulation and Information Hiding in Images. In: Lecture Notes in Computer Science; Vol. 1174, Springer Verlag (1996) 207–226CrossRefGoogle Scholar
  24. 24.
    Swanson, M.D., Kobayashi, M. and Tewfic, A.H.: Multimedia Data Embedding and Watermarking Technologies. In: Proceedings of the IEEE, Vol. 86(6) (1998) 1064–1087CrossRefGoogle Scholar
  25. 25.
    Tanaka, K., Nakamura, Y. and Mitsui, K.: Embedding the Attribute Information into a Dithered Image. In: Systems and Computers in Japan, 21(7) (1990)Google Scholar
  26. 26.
    van Schydel, R., Tirkel, A. and Osborne, C.: A digital Watermarking. In: Proceedings of ICASSP, Piscataway, NJ, IEEE, Vol. II (1994) 86–90Google Scholar
  27. 27.
    Waldemar, P., Ramstad T.: Hibrid KLT-SVD Image Compression. In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, Germany, IEEE Computer Society Press, Vol. 4 (1997) 2713–2716Google Scholar
  28. 28.
    Xia, X.G., Boncelet, C.G. and Arce, G.R.: A Multi-resolution Watermark for Digital Images. In: Proceedings of International Conference on Image Processing (ICIP), Vol.1, USA, IEEE (1997)Google Scholar
  29. 29.
    Yang, J-F., Lu, C-L.: Combined Techniques of Singular Value Decomposition and Vector Quantization for Image Coding. In: IEEE Transaction on Image Processing, Vol. 4(8) (1995) 1141–1146CrossRefGoogle Scholar
  30. 30.
    Zhu, B., Tewfic, A.H. and Gerec, O.: Low Bit Rate Near Transparent Image Coding. In: Proceedings of International Conference on Wavelet Applications for Dual Use, Vol. 2491, Orlando, FL (1995) 173–184Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Vladimir I. Gorodetski
    • 1
  • Leonard J. Popyack
    • 2
  • Vladimir Samoilov
    • 1
  • Victor A. Skormin
    • 3
  1. 1.St. Petersburg Institute for Informatics and AutomationSt. PetersburgRussia
  2. 2.RomeUSA
  3. 3.Watson SchoolBinghamton UniversityBinghamtonUSA

Personalised recommendations