Skip to main content

Steganalysis on All Approaches/Vulnerability Analysis of Stego Image(s)

  • Chapter
  • First Online:
Optimization Models in Steganography Using Metaheuristics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 187))

  • 348 Accesses

Abstract

Stego image quality, secret text embedding capacity, computational time and security are the main challenges involved for steganography methods.

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

Access this chapter

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. Attaby, A.A., Ahmed, M.F.M.M., Alsammak, A.K.: Data hiding inside JPEG images with high resistance to steganalysis using a novel technique: DCT-M3. Ain Shams Eng. J. 9(4), 1965–1974 (2018)

    Article  Google Scholar 

  2. Avcıbas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP J. Appl. Sig. Process. 17, 2749–2757 (2015)

    MATH  Google Scholar 

  3. Banerjee, S., Ghosh, B.R., Roy, P.: JPEG steganography and steganalysis—a review. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds.) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Advances in Intelligent Systems and Computing, vol. 328, pp. 175–187. Springer, Cham (2015)

    Google Scholar 

  4. Chandramouli, R., Kharrazi, M., Memon, N.: Image steganography and steganalysis: concepts and practice. In: Kalker, T., Cox, I., Ro, Y.M. (eds.) Digital Watermarking, IWDW 2003, Lecture Notes in Computer Science (LNCS), vol. 2939, pp. 35–49. Springer, Berlin (2004)

    Chapter  Google Scholar 

  5. Douglas, M., Bailey, K., Leeney, M., Curran, K.: An overview of steganography techniques applied to the protection of biometric data. Multimedia. Tools Appl. 77(13), 17333–17373 (2018)

    Article  Google Scholar 

  6. Fridrich, J.: Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: International Workshop on Information Hiding, Information Hiding, Lecture Notes in Computer Science (LNCS), vol. 3200, pp. 67–81 (2004)

    Chapter  Google Scholar 

  7. Fridrich, J.: Methods for tamper detection in digital images. In: ACM Workshop on Multimedia and Security, Orlando, FL, 30–31 Oct, 1999, pp. 19–23 (1999)

    Google Scholar 

  8. Hedieh, S.: Image steganalysis using Artificial Bee Colony algorithm. J. Exp. Theor. Artif. Intell. 29(5), 949–966 (2017)

    Article  Google Scholar 

  9. Heys, H.M., Tavares, S.E.: Known plaintext cryptanalysis of tree-structured block ciphers. Electron. Lett. 31(10), 784–785 (1995)

    Article  Google Scholar 

  10. Hussain, M., Wahab, A.W.A., Idris, Y.I.B., Ho, A.T.S., Jung, K.: Image steganography in spatial domain: a survey. Sig. Process. Image Commun. 65, 46–66 (2018)

    Article  Google Scholar 

  11. Johnson, N.F., Jajodia, S.: Steganalysis of images created using current steganography software. In: Aucsmith, D. (eds.) Information Hiding, IH 1998, Lecture Notes in Computer Science (LNCS), vol. 1525, pp 273–289. Springer, Berlin (1998)

    Chapter  Google Scholar 

  12. Ker, A.D.: A general framework for structural steganalysis of LSB replacement. In: Information Hiding, Lecture Notes in Computer Science, vol. 3727, pp. 296–311 (2005)

    Chapter  Google Scholar 

  13. Kulkarni, A.J., Durugkar, I.P., Kumar, M.: Cohort intelligence: a self supervised learning behavior. In: Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, 13–16 Oct 2013, pp. 1396–1400. IEEE Computer Society, Washington, DC, USA (2013)

    Google Scholar 

  14. McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)

    Article  MathSciNet  Google Scholar 

  15. Nissar, A., Mir, A.H.: Classification of steganalysis techniques: a study. Digit. Signal Proc. 20(6), 1758–1770 (2010)

    Article  Google Scholar 

  16. Oplatkova, Z., Holoska, J., Zelinka, I., Senkerik, R.: Detection of steganography inserted by outguess and steghide by means of neural networks. In: AMS ‘09 Proceedings of the 2009 Third Asia International Conference on Modelling & Simulation, May 25–29, 2009, pp. 7–12. IEEE Computer Society Washington, DC, USA (2009)

    Google Scholar 

  17. Qin, K., Xu, K., Liu, F., Li, D.: Image segmentation based on histogram analysis utilizing the cloud model. Comput. Math Appl. 62(7), 2824–2833 (2011)

    Article  Google Scholar 

  18. Rabee, A.M., Mohamed, M.H., Mahdy, Y.B.: Blind JPEG steganalysis based on DCT coefficients differences. Multimed. Tools Appl. 77(6), 7763–7777 (2018)

    Article  Google Scholar 

  19. Sadasivam, S., Moulin, P.: On estimation accuracy of desynchronization attack channel parameters. IEEE Trans. Inf. Forensics Secur. 4(3), 284–292 (2009)

    Article  Google Scholar 

  20. Santis, R.D., Montanari, R., Vignali, G., Bottani, E.: An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses. Eur. J. Oper. Res. 267(1), 120–137 (2018)

    Article  MathSciNet  Google Scholar 

  21. Sarmah, D.K., Kulkarni, A.J.: Improved cohort intelligence-a high capacity, swift and secure approach on JPEG image steganography. J. Inf. Secur. Appl. 45, 90–106 (2019)

    Google Scholar 

  22. Sarmah, D., Kulkarni, A.J.: JPEG based steganography methods using cohort intelligence with cognitive computing and modified multi random start local search optimization algorithms. Inf. Sci. 430–431, 378–396 (2018)

    Article  Google Scholar 

  23. Sarmah, D., Kulkarni, A.J.: Image steganography capacity improvement using cohort intelligence and modified multi random start local search methods. Arab. J. Sci. Eng. 43(8), 3927–3950 (2018)

    Article  Google Scholar 

  24. Wang, Z., Zhang, J., Yang, S.: An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals. Swarm Evol. Comput. 51, 100594 (2019)

    Article  Google Scholar 

  25. Westfeld, A.: F5-A steganographic algorithm: high capacity despite better steganalysis. In: Proceedings of the 4th Information Hiding Workshop, LNCS, vol. 2137, pp. 289–302 (2001)

    Chapter  Google Scholar 

  26. Zhihua, X., Xingming, S., Wei, L., Jiaohua, Q., Feng, L.: JPEG Image steganalysis using joint discrete cosine transform domain features. J. Electron. Imaging 19(2), 023006 (2010)

    Article  Google Scholar 

  27. Zong, H., Liu, F., Luo, X.: Blind image steganalysis based on wavelet coefficient correlation. Digit. Investig. 9(1), 58–68 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dipti Kapoor Sarmah .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sarmah, D.K., Kulkarni, A.J., Abraham, A. (2020). Steganalysis on All Approaches/Vulnerability Analysis of Stego Image(s). In: Optimization Models in Steganography Using Metaheuristics. Intelligent Systems Reference Library, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-030-42044-4_7

Download citation

Publish with us

Policies and ethics