Skip to main content
SpringerLink
Log in

Improved Detection of Steganographic Algorithms in Spatial LSB Stego Images Using Hybrid GRASP-BGWO Optimisation

  • Chapter
  • First Online:
Nature Inspired Optimization Techniques for Image Processing Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 150))

Abstract

With the success of passive steganalysis, active steganalysis proceeds with its first step to reveal the steganographic algorithms being used to create the stego images. This process needs to be modelled as a multi-class classification problem. Stem to stern analysis of the literature points out that the existing universal steganalytic features are a thorn in the flesh because of their dimensionality curse (34,671). Hence this work concentrates on detection of steganographic algorithms by optimal novel features christened Local Residual Pattern (LRP) and Local Distance Pattern (LDiP). LRP captures first order derivatives of the high pass filtered output, while LDiP exploits the multi scaled radii neighbourhood to capture deformities at a distance. Acquiring LRP and LDiP from fifteen different kernels, this work focuses to find optimal features by the proposed hybrid technique of Greedy Randomised Adaptive Search—Binary Grey Wolf Optimisation (GRASP-BGWO). Deriving confidence from the bio-inspired algorithm and the divide and conquer approach of the proposed optimisation, this work succeeds in improving the performance of the employed ensemble logistic regression classifier with minimal features. Experimentations conducted using five representative algorithms of spatial Least Significant Bit (LSB) embedding category for eight different payloads show that the developed minimal feature steganalyser outperforms the state-of-the-art steganalysers.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Johnson, N.F.: Steganography software. Available at http://www.jjtc.com/Steganography/tools.html (2012). Accessed 27 Aug 2015

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

    Article  Google Scholar 

  3. Cogranne, R., Retraint, F.: An asymptotically uniformly most powerful test for LSB matching detection. IEEE Trans. Inf. Forensics Secur. 8(3), 464–476 (2013)

    Article  Google Scholar 

  4. Fridrich, J., Kodovskỳ, J.: Steganalysis of LSB replacement using parity-aware features. In: Proceedings of Fourteenth International Conference on Information Hiding, pp. 31–45. Springer, Heidelberg (2013)

    Google Scholar 

  5. Veena, S.T., Arivazhagan, S.: Local descriptor based steganalysis of spatial LSB variant stego images. Presented at the TEQIP II International Conference on Computation, Communication and Innovation, pp. 54–57. ACCET, Karaikudi (2016)

    Google Scholar 

  6. Farhat, F., Ghaemmaghami, S.: Towards blind detection of low-rate spatial embedding in image steganalysis. IET Image Process. 9(1), 31–42 (2015)

    Article  Google Scholar 

  7. Yu, J., Li, F., Cheng, H., Zhang, X.: Spatial steganalysis using contrast of residuals. IEEE Signal Process. Lett. 23(7), 989–992 (2016)

    Article  Google Scholar 

  8. Swaminathan, A., Wu, M., Liu, K.J.R.: Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forensics Sec. 3(1), 101–117 (2008)

    Article  Google Scholar 

  9. Bell, G., Lee, Y.-K.: A method for automatic identification of signatures of steganography software. IEEE Trans. Inf. Forensics Sec. 5(2), 354–358 (2010)

    Article  Google Scholar 

  10. Pevnỳ, T., Fridrich, J.: Towards multi-class blind steganalyzer for JPEG images. In: Digital Watermarking, pp. 39–53. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Pevny, T., Fridrich, J.: Determining the stego algorithm for JPEG images. IEE Proc. Inf. Sec. 153(3), 77 (2006)

    Article  Google Scholar 

  12. Pevny, T., Fridrich, J.: Merging markov and dct features for multi-class jpeg steganalysis. In: Proceedings of SPIE: Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505, pp. 650503–650513 (2007)

    Google Scholar 

  13. Dong, J., Wang, W., Tan, T.: Multi-class blind steganalysis based on image run-length analysis. In: Ho, A.T.S., Shi, Y.Q., Kim, H.J., Barni, M. (eds.) Proceedings on 8th International Workshop on Digital Watermarking IWDW 2009, Guildford, UK, August 24–26, pp. 199–210. Springer, Berlin (2009)

    Chapter  Google Scholar 

  14. Lubenko, I., Ker, A.D.: Steganalysis using logistic regression. In: IS&T/SPIE Electronic Imaging, p. 78800 K (2011)

    Google Scholar 

  15. Zhu, J., Guan, Q., Zhao, X.: Multi-class JPEG image steganalysis by ensemble linear svm classifier. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Yang, C.-N. (eds.) 13th International Workshop on Digital-Forensics and Watermarking (IWDW 2014), Taipei, Taiwan, 1–4 Oct 2014. Revised selected papers, pp. 470–484. Springer International Publishing, Cham (2015)

    Chapter  Google Scholar 

  16. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Sec. 7(3), 868–882 (2012)

    Article  Google Scholar 

  17. Holub, V., Fridrich, J.: Random projections of residuals for digital image steganalysis. IEEE Trans. Inf. Forensics Sec. 8(12), 1996–2006 (2013)

    Article  Google Scholar 

  18. Shi, Y.Q., Sutthiwan, P., Chen, L.: Textural features for steganalysis. In: Information Hiding, pp. 63–77 (2013)

    Chapter  Google Scholar 

  19. Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Trans. Inf. Forensics Secur. 1(1), 111–119 (2006)

    Article  Google Scholar 

  20. Veena, S.T., Arivazhagan, S.: Quantitative steganalysis of spatial LSB based stego images using reduced instances and features. Pattern Recognition Letters. Available online on Aug 2017. https://doi.org/10.1016/j.patrec.2017.08.016

    Article  Google Scholar 

  21. Ker, A.D.: Steganalysis of embedding in two least-significant bits. IEEE Trans. Inf. Forensics Secur. 2(1), 46–54 (2007)

    Article  Google Scholar 

  22. Sharp, T.: An implementation of key-based digital signal steganography. In: International Workshop on Information Hiding, pp. 13–26. Springer, Berlin (2001)

    Chapter  Google Scholar 

  23. Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)

    Article  Google Scholar 

  24. Xu, W.-L., Chang, C.-C., Chen, T.-S., Wang, L.-M.: An improved least-significant-bit substitution method using the modulo three strategy. Displays 42, 36–42 (2016)

    Article  Google Scholar 

  25. Lafferty, P., Ahmed, F.: Texture-based steganalysis: results for color images. In: Proceedings on SPIE 5561, Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications, pp. 145–151 (2004)

    Google Scholar 

  26. Gui, X., Li, X., Yang, B.: Steganalysis of LSB matching based on local binary patterns, pp. 475–480 (2014)

    Google Scholar 

  27. Ker, A.D., Böhme, R.: Revisiting weighted stego-image steganalysis. Electron. Imaging 2008, 681905 (2008)

    Google Scholar 

  28. Fridrich, J., Kodovskỳ, J., Holub, V., Goljan, M.: Steganalysis of content-adaptive steganography in spatial domain. In: Information Hiding, pp. 102–117. Springer, Berlin (2011)

    Chapter  Google Scholar 

  29. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  30. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  31. Lu, J., Liu, F., Luo, X.: Selection of image features for steganalysis based on the fisher criterion. Digit. Invest. 11(1), 57–66 (2014)

    Article  Google Scholar 

  32. Akhavan, S., Akhaee, M.A., Sarreshtedari, S.: Images steganalysis using GARCH model for feature selection. Sig. Process. Image Commun. 39(Part A), 75–83 (2015)

    Article  Google Scholar 

  33. Mohammadi, F.G., Abadeh, M.S.: Image steganalysis using a bee colony based feature selection algorithm. Eng. Appl. Artif. Intell. 31, 35–43 (2014)

    Article  Google Scholar 

  34. Ben Brahim, A., Limam, M.: A hybrid feature selection method based on instance learning and cooperative subset search. Pattern Recogn. Lett. 69, 28–34 (2016)

    Article  Google Scholar 

  35. Moradi, P., Gholampour, M.: A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl. Soft Comput. 43, 117–130 (2016)

    Article  Google Scholar 

  36. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  37. Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371–381 (2016)

    Article  Google Scholar 

  38. Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) Information Hiding, vol. 6958. Springer, Berlin (2011)

    Google Scholar 

  39. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Vojt˘ech Holub for providing the necessary code for comparison. They would also like to express their gratitude to the anonymous editors and reviewers for their helpful suggestions and constructive comments. Also, the authors would like to express their sincere thanks to the Management and Principal of MSEC for providing the necessary facilities and support to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Centre for Image Processing and Pattern Recognition, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

    S. T. Veena, S. Arivazhagan & W. Sylvia Lilly Jebarani

Authors
  1. S. T. Veena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Arivazhagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. W. Sylvia Lilly Jebarani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. T. Veena .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University , Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad , Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Veena, S.T., Arivazhagan, S., Sylvia Lilly Jebarani, W. (2019). Improved Detection of Steganographic Algorithms in Spatial LSB Stego Images Using Hybrid GRASP-BGWO Optimisation. In: Hemanth, J., Balas, V. (eds) Nature Inspired Optimization Techniques for Image Processing Applications. Intelligent Systems Reference Library, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-96002-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96002-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96001-2

  • Online ISBN: 978-3-319-96002-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation