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Cover Image Selection Technique for Secured LSB-based Image Steganography

Published:21 December 2018Publication History

ABSTRACT

Various researches in image steganography focusing on spatial domain specifically in the least Significant Bit (LSB) embedding method had been conducted to improve the embedding capacity while maintaining high imperceptibility. However, these improvements were countered by various statistical attacks. This paper presents an LSB-based image steganography technique that uses YCbCr color space for the embedding process. Also, a new cover selection method to strengthen the proposed embedding algorithm was introduced in this paper. The cover selection mechanism used the skewness and kurtosis of the candidate cover images as factors to determine if a candidate cover image will yield to a high probability of detection or not. The distortions analysis affirms that the stego-images produced by the embedding method obtained acceptable PSNRs and SSIMs, thus, proving that the stego-images are resistant to Human Visual System. Also, the embedding method presented in this paper produced stego-images that were given a low probability of detection of various statistical analysis attacks. Furthermore, the high correlation coefficient between the candidate cover images' skewness and kurtosis and the probability of detection obtained by the stego-images affirms that these properties of the candidate cover images can help in determining the most suitable candidate cover images to be used.

References

  1. Global digital population 2018 | Statistic: 2018. https://www.statista.com/statistics/617136/digital-population-worldwide/. Accessed: 2018-11-30.Google ScholarGoogle Scholar
  2. World Internet Users Statistics and 2018 World Population Stats: 2018. https://www.internetworldstats.com/stats.htm. Accessed: 2018-11-30.Google ScholarGoogle Scholar
  3. Kim, K. 2016. Cryptography: A New Open Access Journal. Cryptography. 1, 1 (2016), 1.Google ScholarGoogle Scholar
  4. Seth, D. et al. 2010. Security Enhancement: Combining Cryptography and Steganography. International Journals of Computer Applications. 9, 11 (2010), 9--12.Google ScholarGoogle Scholar
  5. Parihar, A. and Saxena, A. 2017. Survey on Digital data hiding using Steganography. International Journal of Recent Trends in Engineering and Research. (2017), 237--244.Google ScholarGoogle Scholar
  6. Rote, G.D. and Patil, A.M. 2014. A Novel Approaches Towards Steganography. International Journal of Technical Research and Application. 2, 4 (2014), 72--75.Google ScholarGoogle Scholar
  7. Roy, R. et al. 2013. Evaluating image steganography techniques: Future research Challenges. 2013 International Conference on Computing, Management and Telecommunications, ComManTel 2013. (2013), 309--314.Google ScholarGoogle Scholar
  8. Li, B. et al. 2011. A Survey on Image Steganography and Steganalysis. Journal of Information Hiding and Multimedia Signal Processing. 2, 2 (2011), 142--172.Google ScholarGoogle Scholar
  9. Subhedar, M.S. and Mankar, V.H. 2014. Current status and key issues in image steganography: A survey. Computer Science Review. 13--14, C (2014), 95--113. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Chan, C.K. and Cheng, L.M. 2004. Hiding data in images by simple LSB substitution. Pattern Recognition. 37, 3 (2004), 469--474.Google ScholarGoogle ScholarCross RefCross Ref
  11. Sun, Y. and Liu, F. 2010. Selecting Cover for Image Steganography by Correlation Coefficient. (2010), 159--162.Google ScholarGoogle Scholar
  12. Abbadi, N.K. El 2013. Cover Optimization for Image in Image Steganography. IJCSI International Journal of Computer Science Issues. 10, 1 (2013), 556--564.Google ScholarGoogle Scholar
  13. Subhedar, M.S. and Mankar, V.H. 2017. Curvelet transform and cover selection for secure steganography. Multimed Tools Appl. (2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jegou, H. et al. 2008. Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search - Extended version -. Eccv. 5302, October (2008), 304--317. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Wang, Z. et al. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13, 4 (2004), 600--612. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Fridrich, J. et al. 2001. Detecting LSB Steganography in Color and Gray-Scale Images. IEEE Multimedia and Security. 8, 4 (2001), 22--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Dumitrescu, S. et al. 2003. Detection of LSB steganography via sample pair analysis. IEEE Transactions on Signal Processing. 51, 7 (2003), 1995--2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Westfeld, A. and Pfitzmann, A. 2000. Attacks on steganographic Systems. Information Hiding. 1768, (2000), 1--16. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Cover Image Selection Technique for Secured LSB-based Image Steganography

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        cover image ACM Other conferences
        ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence
        December 2018
        460 pages
        ISBN:9781450366250
        DOI:10.1145/3302425

        Copyright © 2018 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 21 December 2018

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        • Refereed limited

        Acceptance Rates

        ACAI '18 Paper Acceptance Rate76of192submissions,40%Overall Acceptance Rate173of395submissions,44%

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