Skip to main content
SpringerLink
Log in

Steganalysis for clustering modification directions steganography

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

Abstract

In recent time, most of the steganographic methods minimize the embedding cost while maximizing the embedding capacity by injecting message bits in the highly textured regions of the image. Recently, the Clustering Modification Direction (CMD) steganography has been proposed as a wrapper over the additive steganography algorithms, resulting in a substantial improvement in statistical imperceptibility against state-of-the-art steganalytic classifiers. In this paper, a steganalysis scheme, named Selective-Signal-Removal (SSR) is proposed to mount an attack on the CMD algorithm. It has been observed experimentally that the CMD scheme has a tendency to embed in a localized cluster having higher texture. The proposed scheme exploits this fact and tries to predict the embedding zones. It essentially discards the irrelevant region of the image (which may not be modified by the CMD algorithm while embedding) by using a heuristic function with an assignment algorithm to improve the steganalytic detection rate. The experimental results show that the proposed SSR scheme can detect CMD based steganography with improved accuracy.

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
Fig. 7

Similar content being viewed by others

References

  1. Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Transactions on Computers C 23(1):90–93. https://doi.org/10.1109/T-C.1974.223784

    Article  MathSciNet  MATH  Google Scholar 

  2. Bae HJ, Jung SH (1997) Image retrieval using texture based on dct. In: Proceedings of ICICS, 1997 International conference on information, communications and signal processing. Theme: trends in information systems engineering and wireless multimedia communications (Cat., vol 2, pp 1065–1068). https://doi.org/10.1109/ICICS.1997.652144

  3. Bas P, Filler T, Pevný T (2011) Break our steganographic system: the ins and outs of organizing boss. In: Proceedings of the 13th International conference on information hiding, IH’11. http://dl.acm.org/citation.cfm?id=?2042445.2042452. Springer, Berlin, pp 59–70

    Chapter  Google Scholar 

  4. Boroumand M, Chen M, Fridrich J (2018) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14(5):1181–1193

    Article  Google Scholar 

  5. Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensics Secur 6 (3):920–935. https://doi.org/10.1109/TIFS.2011.2134094

    Article  Google Scholar 

  6. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  7. Gujar S, Veni Madhavan C (2009) Measures for classification and detection in steganalysis

  8. Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In: 2012 IEEE International workshop on information forensics and security (WIFS). IEEE, pp 234–239

  9. Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security 2014 (1):1. https://doi.org/10.1186/1687-417X-2014-1

    Article  Google Scholar 

  10. Huang LY (2005) A fast method for textural analysis of dct-based image. J Inf Sci Eng, pp 181–194

  11. Iatan IF (2010) The fisher’s linear discriminant. In: Borgelt C, González-Rodríguez G, Trutschnig W, Lubiano MA, Gil MÁ, Grzegorzewski P, Hryniewicz O (eds) Combining soft computing and statistical methods in data analysis. Springer, Berlin, pp 345–352

  12. Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444. https://doi.org/10.1109/TIFS.2011.2175919

    Article  Google Scholar 

  13. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  14. Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. In: 2014 IEEE International conference on image processing (ICIP). https://doi.org/10.1109/ICIP.2014.7025854, pp 4206–4210

  15. Li B, Wang M, Li X, Tan S, Huang J (2015) A strategy of clustering modification directions in spatial image steganography. IEEE Trans Inf Forensics Secur 10(9):1905–1917

    Article  Google Scholar 

  16. Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Article  Google Scholar 

  17. Pevn? T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: International workshop on information hiding. Springer, pp 161–177

  18. Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. In: Media watermarking, security, and forensics 2015, vol 9409, pp 94090j. International society for optics and photonics

  19. Qian Y, Dong J, Wang W, Tan T (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: 2016 IEEE International conference on image processing (ICIP), pp. 2752–2756. IEEE

  20. Sedighi V, Cogranne R, Fridrich J (2015) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234

    Article  Google Scholar 

  21. Sedighi V, Cogranne R, Fridrich J (2016) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234. https://doi.org/10.1109/TIFS.2015.2486744

    Article  Google Scholar 

  22. Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712

    Article  Google Scholar 

  23. Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12(11):2545–2557

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank the anonymous reviewers for their insightful comments and suggestions. Authors would also like to acknowledge the funding agency, Ministry of Human Resource Development, Government of India.

Author information

Authors and Affiliations

  1. Department of CSE, Indian Institute of Technology Guwahati, Guwahati, India

    Ritvik Rawat, Brijesh Singh, Arijit Sur & Pinaki Mitra

Authors
  1. Ritvik Rawat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brijesh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arijit Sur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pinaki Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brijesh Singh.

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

Rawat, R., Singh, B., Sur, A. et al. Steganalysis for clustering modification directions steganography. Multimed Tools Appl 79, 1971–1986 (2020). https://doi.org/10.1007/s11042-019-08263-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08263-z

Keywords

Search

Navigation