Skip to main content
SpringerLink
Log in

Digital steganography model and embedding optimization strategy

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The existed digital steganography models and theories are not effective enough to guide the steganography processing. Based on previous studies, this paper proposes a complete digital steganography model based on additive noise. And then, with security analysis from KL divergence, the embedding optimization strategy is given through theoretical derivation needless of any side information: optimizing the embedding modification position and optimizing the embedding modification direction (+1 or − 1). Through theoretical derivation, we also obtain the quantitative relationship between the pixels modification probability and the adjacent pixels difference, and prove that modification by ±1 randomly cannot enhance steganographic security definitely. The research in this paper can provide theoretical guidance for the design of steganography algorithms. Compared with previous studies, the proposed embedding optimization strategy has outstanding advantages of being easy to implement and being effective to improve steganographic security. The experiments by optimizing LSBM and MG algorithms show that the proposed embedding optimization strategy can effectively improve each algorithm’s steganographic security at a relative small payload.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bas P, Filler T, Pevný T (2011) Break Our Steganographic System: The Ins and Outs of Organizing BOSS. Information Hiding. Springer Berlin Heidelberg, pp. 59–70

    Chapter  Google Scholar 

  2. Denemark T, Fridrich J (2015) Improving Steganographic Security by Synchronizing the Selection Channel. ACM, pp. 5–14, https://doi.org/10.1145/2756601.2756620

  3. Denemark T, Fridrich J (2015) Side-informed steganography with additive distortion. IEEE International Workshop on Information Forensics and Security. IEEE, pp. 1–6, https://doi.org/10.1109/WIFS.2015.7368589

  4. Denemark T, Fridrich J (2017) Model Based Steganography with Precover. Electronic Imaging 2017(7):56–66. https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-326

    Article  Google Scholar 

  5. FILLER T, FRIDRICH J (2010) Gibbs construction in steganography. IEEE Transactions on Information Forensics and Security 5(4):705–720. https://doi.org/10.1109/TIFS.2010.2077629

    Article  Google Scholar 

  6. Filler T, Judas J, Fridrich J (2010) Minimizing embedding impact in steganography using trellis-coded quantization. Proceedings of SPIE - The International Society for Optical Engineering 6(1):175–178. https://doi.org/10.1117/12.838002

    Article  Google Scholar 

  7. Fridrich J (2015) Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model. Proceedings of SPIE - The International Society for Optical Engineering, 9409: 94090H-94090H-13, https://doi.org/10.1117/12.2080272

  8. Fridrich J, Kodovský J (2012) Rich Models for Steganalysis of Digital Images. IEEE Transaction on Information Forensics and Security 7(3):868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  9. Fridrich J, Kodovský J (2013) Multivariate gaussian model for designing additive distortion for steganography. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp. 2949–2953, https://doi.org/10.1109/ICASSP. 2013. 6638198

  10. Ker AD (2009) Estimating Steganographic Fisher Information in Real Images. Information Hiding, International Workshop, Ih 2009, Darmstadt, Germany, June 8–10, 2009, Revised Selected Papers. DBLP, pp. 73–88, https://doi.org/10.1007/978-3-642-04431-1_6

    Chapter  Google Scholar 

  11. Kodovsky J, Fridrich J, Holub V (2012) Ensemble Classifiers for Steganalysis of Digital Media. IEEE Transactions on Information Forensics & Security 7(2):432–444. https://doi.org/10.1109/TIFS.2011.2175919

    Article  Google Scholar 

  12. Li B, Tan S, Wang M, et al (2015) A strategy of clustering modification directions in spatial Image steganography. Information Forensics and Security, IEEE Transactions on, 10(9):1905–1917, https://doi.org/10.1109/TIFS.2015. 2434600

  13. Luo X, Song X, Li X et al (2016) Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes. Multimedia Tools & Applications 75(21):13557–13583. https://doi.org/10.1007/s11042-015-2759-2

    Article  Google Scholar 

  14. Ma Y, Luo X, Li X et al (2018) Selection of Rich Model Steganalysis Features Based on Decision Rough Set α-Positive Region Reduction. IEEE Transactions on Circuits & Systems for Video Technology PP(99):1. https://doi.org/10.1109/TCSVT.2018.2799243

    Article  Google Scholar 

  15. Sedighi V, Cogranne R, Fridrich J (2015) Content-Adaptive Steganography by Minimizing Statistical Detectability. IEEE Transactions on Information Forensics & Security 11(2):221–234. https://doi.org/10.1109/TIFS.2015.2486744

    Article  Google Scholar 

  16. Sun Y, Niu D, Tang G et al (2012) Optimized LSB Matching Steganography Based on Fisher Information. J Multimed 7(4):295–302. https://doi.org/10.4304/jmm.7.4.295-302

    Article  Google Scholar 

  17. Xi L, Ping XJ, Zhang H (2011) Security Analysis of Adaptive Steganography Basedon Gaussian Mixture Model. Journal of Information Engineering University 12(3):333–337

    Google Scholar 

  18. Yang C, Luo X, Lu J, Liu F (2018) Extracting hidden messages of MLSB steganography based on optimal stego subset. SCIENCE CHINA Inf Sci 61(11):1–3. https://doi.org/10.1007/s11432-017-9328-2

    Article  Google Scholar 

  19. Zhang Y, Qin C, Zhang W et al (2018) On the Fault-tolerant Performance for a Class of Robust Image Steganography. Signal Process 146:99–111. https://doi.org/10.1016/j.sigpro.2018.01.011

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zhengzhou Information Science and Technology Institute, Zhengzhou, People’s Republic of China

    Hai-Tao Song, Guang-Ming Tang, Yi-Feng Sun & Ming-Ming Jiang

  2. National Innovation Institute of Defense Technology, Beijing, People’s Republic of China

    Guang Kou

Authors
  1. Hai-Tao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guang-Ming Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang Kou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi-Feng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming-Ming Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guang Kou.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, HT., Tang, GM., Kou, G. et al. Digital steganography model and embedding optimization strategy. Multimed Tools Appl 78, 8271–8288 (2019). https://doi.org/10.1007/s11042-018-6810-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6810-y

Keywords

Search

Navigation