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

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Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012)

Part of the book series: Lecture Notes in Bioengineering ((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.

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References

  1. Katzenbeisser S, Fabien AP (eds) (2000) Information hiding techniques for steganography and digital watermarking. Artech House Publishers, p 1

    Google Scholar 

  2. Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann, p 11

    Google Scholar 

  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 360–363

    Google Scholar 

  4. Li X, Wang J (2007) A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf Sci 177(15):3099–3109

    Article  Google Scholar 

  5. Sasaki H (ed) (2007) Intellectual Property Protection For Multimedia Information Technology. IGI Global, p 12

    Google Scholar 

  6. Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. IEEE Comp J 31(2):26–34

    Article  Google Scholar 

  7. Fridrich J, Goljan M (2001) Practical steganalysis of digital images—state of the art

    Google Scholar 

  8. Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recognition Letter 34:671–683

    Google Scholar 

  9. Hwang MS, Chang CC, Hwang KF (2000) Digital watermarking of images using neural networks. J Electron Imaging 9:548–555

    Google Scholar 

  10. Yu PT, Tsai HH, Lin JS (2001) Digital watermarking based on neural networks for color images. Signal processing 81:663–671

    Google Scholar 

  11. Aveibas I, Memon N, Sankur B (2001) Steganalysis based on image quality metrics. In: IEEE fourth workshop on multimedia signal processing, pp 517–522

    Google Scholar 

  12. Wang RZ, Lin CF, Lin JC (2000) Hiding data in images by optimal moderately significant-bit replacement. IEE Electronics Letter 36:2069–2070

    Google Scholar 

  13. Chan CK, Cheng LM (2001) Improved hiding data in images by optimal moderately significant-bit replacement. IEE Electronics Letter 37:1017–1018

    Google Scholar 

  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 27:7–33

    Google Scholar 

  15. Voloshynovskiy S, Herrigel A, Rytsar Y (2002) StegoWall: blind statistical detection of hidden data. Proceedings SPIE 4675:57–68

    Google Scholar 

  16. Rumelhart D, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1: Foundations. MIT Press Cambridge, MA

    Google Scholar 

  17. Bender W, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3/4):313–336

    Article  Google Scholar 

  18. Chan CK, Cheng LM (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37(3):469–474

    Article  MATH  Google Scholar 

  19. Chang CC, Hsiaob JY, Chan CS (2003) Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy. Pattern Recognit 36:1583–1595

    Article  Google Scholar 

  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 273–289

    Google Scholar 

  21. Fridrich J, Goljan M, Du R (2001) Steganalysis based on JPEG compatibility, SPIE multimedia systems and applications IV, 20–24 Aug 2001

    Google Scholar 

  22. Kahn D (1996) The history of steganography. In: Proceedings of the first international workshop on information hiding. Springer-Verlag, London, pp 1–5

    Google Scholar 

  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. Johnson NF, Jajodia S (1998) Steganalysis: “the investigation of hidden information”. Proceedings of the IEEE Information Technology Conference, Syracuse, New York, USA

    Google Scholar 

  25. Marvel LM, Boncelet CG, Retter CT (1999) Spread spectrum image steganography. IEEE Trans Image Process 8(8):1075–1083

    Article  Google Scholar 

  26. Schneier B (1996) Applied cryptography second edition: protocols, algorithms, and source code in C. Wiley,  

    Google Scholar 

  27. Wang RZ, Lin CF, Lin JC (2000) Hiding data in images by optimal moderately significant-bit replacement. IEE Electron Lett 36(25):2069–2070

    Article  Google Scholar 

  28. Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recogn 34(3):671–683

    Article  MATH  Google Scholar 

  29. Rosenblatt F (1958) The perceptron a probabilistic model for information storage and organization. Brain Psych Rev 62:386–408

    Google Scholar 

  30. Chen TS, Chang CC, Hwang MS (1998) A virtual image cryptosystem based upon vector quantization. IEEE Trans Image Process 7(10):1485–1488

    Article  MathSciNet  MATH  Google Scholar 

  31. Chung KL, Shen CH, Chang LC (2001) A novel SVD- and VQ-based image hiding scheme. Pattern Recognit Lett 22(9):1051–1058

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE, TIT, Bhopal, India

    Kirti Bala Bahekar, Praneet Saurabh & Bhupendra Verma

Authors
  1. Kirti Bala Bahekar
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  2. Praneet Saurabh
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  3. Bhupendra Verma
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Corresponding author

Correspondence to Kirti Bala Bahekar .

Editor information

Editors and Affiliations

  1. Jabalpur Engineering College, Kundam Road, Jabalpur, 482011, Madhya Pradesh, India

    Veerendra Kumar

  2. , Computer Science, Gyan Ganga College of Technology, Tilwara Ghat, Garha, Near Shah Nala Tripuri Ward, Jabalpur, 482003, Madhya Pradesh, India

    Mukta Bhatele

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Bahekar, K.B., Saurabh, P., Verma, B. (2013). Improving High Embedding Capacity Using Artificial Immune System: A Novel Approach for Data Hiding. In: Kumar, V., Bhatele, M. (eds) Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012). Lecture Notes in Bioengineering. Springer, India. https://doi.org/10.1007/978-81-322-0970-6_24

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  • DOI: https://doi.org/10.1007/978-81-322-0970-6_24

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-0969-0

  • Online ISBN: 978-81-322-0970-6

  • eBook Packages: EngineeringEngineering (R0)

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