Advertisement

Steganalysis for clustering modification directions steganography

  • Ritvik Rawat
  • Brijesh SinghEmail author
  • Arijit Sur
  • Pinaki Mitra
Article
  • 44 Downloads

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.

Keywords

Steganalysis Spatial domain steganalysis CMD steganography 

Notes

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.

References

  1. 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 MathSciNetCrossRefzbMATHGoogle Scholar
  2. 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. 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
  4. 4.
    Boroumand M, Chen M, Fridrich J (2018) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14(5):1181–1193CrossRefGoogle Scholar
  5. 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 CrossRefGoogle Scholar
  6. 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 CrossRefGoogle Scholar
  7. 7.
    Gujar S, Veni Madhavan C (2009) Measures for classification and detection in steganalysisGoogle Scholar
  8. 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 234–239Google Scholar
  9. 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 CrossRefGoogle Scholar
  10. 10.
    Huang LY (2005) A fast method for textural analysis of dct-based image. J Inf Sci Eng, pp 181–194Google Scholar
  11. 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 345–352Google Scholar
  12. 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 CrossRefGoogle Scholar
  13. 13.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  14. 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. 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):1905–1917CrossRefGoogle Scholar
  16. 16.
    Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224CrossRefGoogle Scholar
  17. 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 161–177Google Scholar
  18. 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 photonicsGoogle Scholar
  19. 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. IEEEGoogle Scholar
  20. 20.
    Sedighi V, Cogranne R, Fridrich J (2015) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 22.
    Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712CrossRefGoogle Scholar
  23. 23.
    Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12(11):2545–2557CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSEIndian Institute of Technology GuwahatiGuwahatiIndia

Personalised recommendations