Advertisement

A Variable Neighborhood Search-Based Method with Learning for Image Steganography

  • Dalila BoughaciEmail author
  • Hanane Douah
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Image steganography is a security technique that used to hide secret information such as text or image in another cover image. The cover image, including the secret information, seems to be unchanged, and the hidden information can only be recovered by using a particular decoding technique. This paper proposes a variable neighborhood search (VNS)-based method for image steganography. The proposed VNS is combined with the least significant bits method (LSB) and enhanced with a learning process. LSB is the process of adjusting the lower bits of the pixels of the cover image. The least significant bit which is the eighth bit of some or all bytes inside the cover image is replaced by bits of the secret information. We improve LSB by combining it with VNS. The VNS method is a local search meta-heuristic working on a set of different neighborhoods. The basic idea is a systematic change of a certain number of neighborhoods combined with a local search. The objective is to explore the search space efficiently in order to locate the appropriate positions in the cover image where inserting the secret information. Further, a learning process is added to VNS in order to enhance the performance. The proposed methods are evaluated on some series of images. The numerical results are exciting and demonstrate the benefits of the new techniques for image steganography.

Keywords

Image steganography Security Optimization Local search Variable neighborhood search Meta-heuristics 

References

  1. Arjun, S., Negi, A., Kranthi, C., Keerthi, D.: An approach to adaptive steganography based on matrix embedding. In: TENCON 2007-2007 IEEE Region 10 Conference (University College of Engineering (A), Hyderabad) (2007)Google Scholar
  2. Boughaci, D., Benhamou, B., Drias, H.: Local search methods for the optimal winner determination problem. J. Math. Model. Algorithms 9(2), 165–180. http://www.springerlink.com/content/hv637861870mx8j4/ (2010)
  3. Boughaci, D., Kemouche, A., Lachibi, H.: Stochastic local search combined with LSB technique for image steganography. In: 2016 13th Learning and Technology Conference (L and T)Google Scholar
  4. Cachin, C.: An Information-Theoretic Model for Steganography, Cryptology. ePrint Archive, Report 2000/028. www.zurich.ibm.com/˜cca/papers/stego.pdf (2002)
  5. Das, S., Das, S., Bandyopadhyay, B., Sanyal, S.: Steganography and steganalysis: different approaches. Int. J. Comput. Inform. Technol. Eng. (IJCITAE) 2(1) (2008)Google Scholar
  6. El Shafie, D.R., Kharma, N., Ward, R.: Parameter optimization of an embedded watermark using a genetic algorithm. In: International Symposium on Communications, Control and Signal Processing, ISCCSP, pp 1263–1267, St Julians, 12–14 Mar 2008Google Scholar
  7. Ford, A., Roberts, A.: Colour Space Conversions. http://www.poynton.com/PDFs/coloureq.pdf (1998)
  8. Fridrich, J., Goljan, M., Du, R.: Reliable detection of LSB steganography in color and grayscale images. In: Proceedings of the 2001 Workshop on Multimedia and Security New Challenges—MM-Sec ’01, 27. ACM Press, New York, New York, USA (2001).  https://doi.org/10.1145/1232454.1232466
  9. Fridrich, J., Goljan, M., Hogea, D.: Steganalysis of JPEG Images: Breaking the F5 Algorithm. SUNY Binghamton, Binghamton, NY, 13902-6000, USA (2002)Google Scholar
  10. Hoos, H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann (2005)Google Scholar
  11. Inoue, H., Miyazaki, A., Katsura, T.: An image watermarking method based on the wavelet transform. In: International Conference on Image Processing, vol. 1, pp. 296–300. IEEE ICIP, Kobe (2002)Google Scholar
  12. Kavitha, K.K., Koshti, A., Dunghav, P.: Steganography using least significant bit algorithm. Int. J. Eng. Res. Appl. 2(3), 338–341 (2012)Google Scholar
  13. Li, B., He, J., Huang, J., Shi, Y.Q.: A survey on image steganography and steganalysis. J. Inform. Hiding Multimed. Signal Process. 2(2) (ISSN 2073-4212, Ubiquitous International) April 2011Google Scholar
  14. Mladenovi, N., Hansen, P.: Variable neighbourhood decomposition search. Computer and Operations Research, vol. 24, pp. 1097–1110 (1997)Google Scholar
  15. Nosrati, M., Karimi, R., Hariri, M.: Embedding Stego- text in cover images using linked List concepts and LSB technique. World Appl. Progr. 1(4), 264–268 (2011)Google Scholar
  16. Ouaddah, A., Boughaci, D.: Local Search Method for Image Reconstruction with Same Concentration in Tomography, 335–346. SIRS (2014)Google Scholar
  17. Provos, N., Honeyman, P.: Hide and seek: an introduction to steganography. IEEE Secur. Priv. (2003)Google Scholar
  18. Sachnev, V., Kim, H.J., Zhang, R.: Less detectable JPEG steganography method based on heuristic optimization and BCH syndrome coding, MM Sec ’09. Princeton, NJ, USA, Sept 2009Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science DepartmentLRIA-FEI- USTHBAlgiersAlgeria

Personalised recommendations