Improving High Embedding Capacity Using Artificial Immune System: A Novel Approach for Data Hiding

  • Kirti Bala Bahekar
  • Praneet Saurabh
  • Bhupendra Verma
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

Data hiding is a method of hiding secret messages into a cover media such that an unintended observer will not be aware of the existence of the hidden messages. Various embedding mechanisms are provided with different robustness capacity. This mechanism has two major categories of embedding data. In addition, it is observed that the unevenly distributed embedding capacity brings difficulty in data hiding. Thus, a comprehensive solution to this problem is addressing the considerations for choosing constant or variable embedding rate and enhancing the performance for each case. This chapter deal with Artificial Immune system technique to text, audio, image, and video data without embedding any information into the target content trained on frequency domain. Proposed method can detect a hidden bit codes from the content Hidden codes are retrieved from the AIS network only with the proper extraction key provided. The goal of this chapter is to be able to introduce Damage less information hiding scheme with no damage to the target content using Artificial Immune System.

Keywords

Data hiding AIS Damage less information 

References

  1. 1.
    Katzenbeisser S, Fabien AP (eds) (2000) Information hiding techniques for steganography and digital watermarking. Artech House Publishers, p 1Google Scholar
  2. 2.
    Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann, p 11Google Scholar
  3. 3.
    Chang CC, Tseng HW (2009) Data hiding in images by hybrid LSB substitution. In: 3rd international conference on multimedia and ubiquitous engineering, article no. 5318917, pp 360–363Google Scholar
  4. 4.
    Li X, Wang J (2007) A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf Sci 177(15):3099–3109CrossRefGoogle Scholar
  5. 5.
    Sasaki H (ed) (2007) Intellectual Property Protection For Multimedia Information Technology. IGI Global, p 12Google Scholar
  6. 6.
    Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. IEEE Comp J 31(2):26–34CrossRefGoogle Scholar
  7. 7.
    Fridrich J, Goljan M (2001) Practical steganalysis of digital images—state of the artGoogle Scholar
  8. 8.
    Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recognition Letter 34:671–683Google Scholar
  9. 9.
    Hwang MS, Chang CC, Hwang KF (2000) Digital watermarking of images using neural networks. J Electron Imaging 9:548–555Google Scholar
  10. 10.
    Yu PT, Tsai HH, Lin JS (2001) Digital watermarking based on neural networks for color images. Signal processing 81:663–671Google Scholar
  11. 11.
    Aveibas I, Memon N, Sankur B (2001) Steganalysis based on image quality metrics. In: IEEE fourth workshop on multimedia signal processing, pp 517–522Google Scholar
  12. 12.
    Wang RZ, Lin CF, Lin JC (2000) Hiding data in images by optimal moderately significant-bit replacement. IEE Electronics Letter 36:2069–2070Google Scholar
  13. 13.
    Chan CK, Cheng LM (2001) Improved hiding data in images by optimal moderately significant-bit replacement. IEE Electronics Letter 37:1017–1018Google Scholar
  14. 14.
    Chen B, Wornell GW (2001) Implementations of quantization index modulation methods for digital watermarking and information embedding of multimedia. Special Issue Multimedia Signal Processing 27:7–33Google Scholar
  15. 15.
    Voloshynovskiy S, Herrigel A, Rytsar Y (2002) StegoWall: blind statistical detection of hidden data. Proceedings SPIE 4675:57–68Google Scholar
  16. 16.
    Rumelhart D, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1: Foundations. MIT Press Cambridge, MAGoogle Scholar
  17. 17.
    Bender W, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3/4):313–336CrossRefGoogle Scholar
  18. 18.
    Chan CK, Cheng LM (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37(3):469–474MATHCrossRefGoogle Scholar
  19. 19.
    Chang CC, Hsiaob JY, Chan CS (2003) Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy. Pattern Recognit 36:1583–1595CrossRefGoogle Scholar
  20. 20.
    Johnson NF, Jajodia S (1998) Steganalysis of images created using current steganography software. Lecture notes in computer science, vol 1525. Springer-Verlag, Berlin, pp 273–289Google Scholar
  21. 21.
    Fridrich J, Goljan M, Du R (2001) Steganalysis based on JPEG compatibility, SPIE multimedia systems and applications IV, 20–24 Aug 2001Google Scholar
  22. 22.
    Kahn D (1996) The history of steganography. In: Proceedings of the first international workshop on information hiding. Springer-Verlag, London, pp 1–5Google Scholar
  23. 23.
    Kelley J (2001) Terror groups hide behind Webencryption. USA Today, Feburary 2001. http://www.usatoday.com/life/cyber/tech/2001-02-05-binladen.htm
  24. 24.
    Johnson NF, Jajodia S (1998) Steganalysis: “the investigation of hidden information”. Proceedings of the IEEE Information Technology Conference, Syracuse, New York, USAGoogle Scholar
  25. 25.
    Marvel LM, Boncelet CG, Retter CT (1999) Spread spectrum image steganography. IEEE Trans Image Process 8(8):1075–1083CrossRefGoogle Scholar
  26. 26.
    Schneier B (1996) Applied cryptography second edition: protocols, algorithms, and source code in C. Wiley,  Google Scholar
  27. 27.
    Wang RZ, Lin CF, Lin JC (2000) Hiding data in images by optimal moderately significant-bit replacement. IEE Electron Lett 36(25):2069–2070CrossRefGoogle Scholar
  28. 28.
    Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recogn 34(3):671–683MATHCrossRefGoogle Scholar
  29. 29.
    Rosenblatt F (1958) The perceptron a probabilistic model for information storage and organization. Brain Psych Rev 62:386–408Google Scholar
  30. 30.
    Chen TS, Chang CC, Hwang MS (1998) A virtual image cryptosystem based upon vector quantization. IEEE Trans Image Process 7(10):1485–1488MathSciNetMATHCrossRefGoogle Scholar
  31. 31.
    Chung KL, Shen CH, Chang LC (2001) A novel SVD- and VQ-based image hiding scheme. Pattern Recognit Lett 22(9):1051–1058MATHCrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Kirti Bala Bahekar
    • 1
  • Praneet Saurabh
    • 1
  • Bhupendra Verma
    • 1
  1. 1.Department of CSETITBhopalIndia

Personalised recommendations