Skip to main content
SpringerLink
Log in

Secure steganography based on embedding capacity

  • Regular Contribution
  • Published:
International Journal of Information Security Aims and scope Submit manuscript

Abstract

Mostly the embedding capacity of steganography methods is assessed in non-zero DCT coefficients. Due to unequal distribution of non-zero DCT coefficients in images with different contents, images with the same number of non-zero DCT coefficients may have different actual embedding capacities. This paper introduces embedding capacity as a property of images in the presence of multiple steganalyzers, and discusses a method for computing embedding capacity of cover images. Using the capacity constraint, embedding can be done more secure than the state when the embedder does not know how much data can be hidden securely in an image. In our proposed approach, an ensemble system that uses different steganalyzer units determines the security limits for embedding in cover images. In this system, each steganalyzer unit is formed by a combination of multiple steganalyzers from the same type, which are sensitive to different payloads. The confidence of each steganalyzer on an image leads us to determine the upper bound of embedding rate for the image. Considering embedding capacity, the steganographer can minimize the risk of detection by selecting a proper cover image that is secure for a certain payload. Moreover, we analyzed the relation between complexity and embedding capacity of images. Experimental results showed the effectiveness of the proposed approach in enhancing the security of stego images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Marvel, L.M.: Spread spectrum image steganography. IEEE Trans. Image Process. pp. 1075–1083 (1999)

  2. Westfeld, A.: F5-a steganographic algorithm: high capacity despite better steganalysis. In: Proceedings of 4th International Workshop on Information Hiding (2001)

  3. Sallee, P.: Model-based steganography. In: Proceedings of Intetrnational Workshop on Digital Watermarking, Seoul, Korea (2003)

  4. Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography with wet paper codes. In: Proceedings of ACM, Multimedia Workshop, Germany (2004)

  5. Solanki, K., Sarkar, A., Manjunath, B.S.: YASS: yet another steganographic scheme that resists blind steganalysis. In: Proceedings of 9th International Workshop on Information Hiding, June (2007)

  6. Sajedi, H., Jamzad, M.: Adaptive Steganography Method Based on Contourlet Transform. In: Proceedings of 9th International Conference on Signal Processing, pp. 745–748 (2008)

  7. Chandramouli R., Memon N.D.: Steganography capacity: a steganalysis perspective. Proc. SPIE Secur. Watermarking Multimed. Contents 5020, 173–177 (2003)

    Google Scholar 

  8. Fridrich J., Pevny T., Kodovsky J.: Statistically Undetectable JPEG Steganography: Dead Ends, Challenges, and Opportunities. MM&Sec. ACM, Dallas (2007)

    Google Scholar 

  9. Cachin, C.: An information-theoretic model for steganography. In: Proceedings of 2nd International Workshop on Information Hiding. LNCS, vol. 1525, pp. 306–318 (1998)

  10. Moulin P., Mihcak M.K.: A framework for evaluating the data hiding capacity of image sources. IEEE Trans. Image Process. 11, 1029–1042 (2002)

    Article  Google Scholar 

  11. Dong, Y., Han, K.: Boosting SVM classifiers by ensemble. In: Proceedings of 14th International Conference of ACM on World Wide Web, pp. 1072–1073 (2005)

  12. Ker A.D.: A Batch steganography and pooled steganalysis. Proc. Inf. Hiding Workshop 4437, 265–281 (2006)

    Article  Google Scholar 

  13. Ker A.D.: A capacity result for batch steganography. IEEE Signal Process. Let. 14(8), 525–528 (2007)

    Article  Google Scholar 

  14. Martin, A., Sapiro, G., Seroussi, G.: Is Image Steganography Natural? Technical Report, Information Theory Research Group, HP Laboratories Palo Alto (2004)

  15. Kharrazi M., Sencar T.H., Memon N.: Benchmarking steganographic and steganalysis, techniques. EI SPIE, San Jose (2005)

    Google Scholar 

  16. Avcibas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP J. Appl. Signal Process. (2005)

  17. Lyu, S., Farid, H.: Detecting hidden messages using higher-order statistics and support vector machines. In: Proceedings of 5th International Workshop on Information Hiding (2002)

  18. Lyu S., Farid H.: Steganalysis using color wavelet statistics and one-class support vector machines. SPIE Symposium on Electronic Imaging, San Jose, CA (2004)

    Google Scholar 

  19. Fridrich, J.: Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. In: Proceedings of 6th International Workshop on Information Hiding, Toronto (2004)

  20. Dietterich, T.G.: Ensemble methods in machine learning. Multiple Classifier Systems. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2001)

  21. Freund Y., Schapire R.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)

    Google Scholar 

  22. Qing T., Jue W.: A new fuzzy support vector machine based on the weighted margin. Neural Process. Lett. 20(3), 139–150 (2004)

    Article  Google Scholar 

  23. Zhang, L., Lin, F., Zhang, B.: Support vector machine learning for image retrieval. In: Proceedings of IEEE International Conference on Image Processing, pp. 721–724 (2001)

  24. Burges C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)

    Article  Google Scholar 

  25. Meyer D., Leisch F., Hornik K.: The support vector machine under test. Neurocomputing 55, 169–186 (2003)

    Article  Google Scholar 

  26. Pevny T., Fridrich J.: Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proceedings of SPIE, San Jose, CA (2007)

  27. Sarkar, A., Solanki, K., Manjunath, B.S.: Further study on YASS: steganography based on randomized embedding to resist blind steganalysis. In: Proceedings of SPIE Security, Steganography, and Watermarking of Multimedia Contents (2008)

  28. Chen, C., Shi, Y.Q., Chen, W., Xuan, G.: Statistical moments based universal steganalysis using JPEG-2D array and 2-D characteristic function. In: Proceedings of ICIP, Atlanta, GA, USA, pp. 105–108 (2006)

  29. Xuan, G., Shi, Y.Q., Gao, J., Zou, D., Yang, C., Yang, C., Zhang, Z., Chai, P., Chen, C., Chen, W.: Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: Proceedings of 7th International Workshop on Information Hiding (2005)

  30. Kermani, Z.Z., Jamzad, M.: A robust steganography algorithm based on texture similarity using gabor filter. In: Proceedings of IEEE Intrnational Symposium on Signal processing and Information Technology, pp. 578–582 (2005)

  31. Sajedi, H., Jamzad, M.: Cover selection steganography method based on similarity of image blocks. In: Proceedings of IEEE 8th CIT Conference, Sydney, Australia (2008)

  32. Kharrazi, M., Sencar, H., Memon, N.: Cover Selection for steganograpic embedding. In: Proceedings of ICIP, pp. 117–121 (2006)

  33. Watson, B.A.: DCT quantization matrices visually optimized for individual images, human vision. In: Visual Processing and Digital Display IV, Proceedings of SPIE, vol. 1913, pp. 202–216 (2005)

  34. Wang Z., Bovik A., Sheikh H., Simoncelli E.: Perceptual image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  35. http://www.cs.washington.edu/research/imagedatabase

  36. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machine. 2007. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  37. Cho K., Jang J., Hong K.: Adaptive skin-color filter. Pattern Recognit. 34(5), 1067–1073 (2001)

    Article  MATH  Google Scholar 

  38. Jamzad M., Yaghmaee F.: Achieving higher stability in watermarking according to image complexity. Sci. Iran. J. 13(4), 404–412 (2006)

    Google Scholar 

  39. Haller, R.S.: Complexity of Real Images Evaluated by Densitometric Analysis and by Psychophysical Scaling. M.Sc. Thesis, University of Arizona (1970)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Hedieh Sajedi & Mansour Jamzad

Authors
  1. Hedieh Sajedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mansour Jamzad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hedieh Sajedi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sajedi, H., Jamzad, M. Secure steganography based on embedding capacity. Int. J. Inf. Secur. 8, 433–445 (2009). https://doi.org/10.1007/s10207-009-0089-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10207-009-0089-y

Keywords

Search

Navigation