RGB channel based decision tree grey-alpha medical image steganography with RSA cryptosystem

Original Article

Abstract

This paper presents the novelty in sensitive data transmission of patient medical records. The secret medical data is hidden inside scanned grey medical image or magnetic resonance image using the red, green, blue, and alpha (RGBA) image and with the help of decision tree. In this technique, alpha channel will be separated from the RGBA image and merged to the medical grey image to improve the hiding capacity. RSA cryptosystem is used to encrypt the medical data, and divided into various blocks using dynamic key. In steganography process, organize the grey-alpha channel medical cover image into various blocks using dynamic key. Secret cipher blocks are assigned to grey-alpha channel medical cover image blocks using Breadth First Search and decision tree, for data embedding. Performance analysis is observed using various performance measure parameters between various medical stego and cover images.

Keywords

Decision tree RGB channel Grey-alpha channel Steganography Cryptography Encryption Decryption Embedding 

References

  1. 1.
    Randolph C, Barrows JR, Paul MD, Clayton D (1996) Review: privacy, confidentiality and electronic medical records. J Am Med Inf Assoc 3(2):139–148. Available at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC116296/pdf/0030139.pdf
  2. 2.
    Raman RS, Reddy R, Jagannathan V, Reddy S, Cleetus KJ, Srinivas K (1997) A strategy for the development of secure telemedicine applications. In: Proceedings of the AMIA annual fall symposium, pp 344–348. Available at http://www.ncbi.nlm.nih.gov/pubmed/9357645
  3. 3.
    United S (2000) Summary of the HIPAA privacy rule. United States Department of Health and Human Services, pp 1–19. Available at http://www.hhs.gov/ocr/privacy/hipaa/understanding/summary/privacysummary.pdf
  4. 4.
    Chandra MK, Cherif A (2002) Implementation of the RSA algorithm and its cryptanalysis. In: ASEE Gulf-Southwest annual conference, American society for engineering education, USA. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.6258&rep=rep1&type=pdf
  5. 5.
    Wu DC, Tsai WH (2003) A steganograhic method for images by pixel value differencing. Pattern Recogn Lett 24(9-10):1613–1626CrossRefMATHGoogle Scholar
  6. 6.
    Zhang X, Wang S (2004) Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security. Pattern Recogn Lett 25(12):331–339CrossRefGoogle Scholar
  7. 7.
    Chang CC, Tseng HW (2004) A steganographic method for digital images using side match. Pattern Recogn Lett 25(12):1431–1437CrossRefGoogle Scholar
  8. 8.
    Martin A, Sapiro G, Seroussi G (2005) Is image steganography natural. IEEE Trans Image Process 14(12):2040–2050CrossRefGoogle Scholar
  9. 9.
    Wang R, Chen Y (2006) High payload image steganography using two-way block matching. IEEE Signal Process Lett 13(3):161–164CrossRefGoogle Scholar
  10. 10.
    Kumar PM, Roopa D (2007) An image steganography framework with improved tamper proofing. Asian J Inf Technol 6(10):1023–1029Google Scholar
  11. 11.
    Provos N, Honeyman P (2003) Hide and seek: an introduction to steganography. Secur Privacy Mag IEEE 1(3):32–44CrossRefGoogle Scholar
  12. 12.
    Cheddad A et al (2010) Digital image steganography survey and analysis of current methods. Signal Process 90:727–752CrossRefMATHGoogle Scholar
  13. 13.
    Mohammad ABY, Jantan A (2008) A new steganography approach for image encryption exchange by using the LSB insertion. IJCSNS Int J Comput Sci Netw Secur 8(6):247–254. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.371.9525&rep=rep1&type=pdf
  14. 14.
    Nag A, Singh JP, Khan S, Ghosh S (2011) A weighted location based LSB image steganography technique. Springer ACC 2011, CCIS (ISBN: 978-3-642-22714-1), 2(191):620–627. Available at http://www.link.springer.com/content/pdf/10.1007/978-3-642-22714-1_64.pdf
  15. 15.
    Maiti C, Baksi D, Zamider I, Gorai P, Kisku DR (2011) Data hiding in images using some efficient steganography techniques. Springer SIP 2011, CCIS (ISBN: 978-3-642-27183-0), 2(260):195–203. Available at http://www.link.springer.com/chapter/10.1007%2F978-3-642-27183-0_21
  16. 16.
    Juneja M, Sandhu PS (2009) Designing of robust steganography technique based on LSB insertion and encryption. In: Proceedings of international conference on advances in recent technologies in communication and computing (ISBN: 978-0-7695-38457), pp 302–305. Available at http://www.dl.acm.org/citation.cfm?id=1673335
  17. 17.
    Parvez MT, Gutub AA (2008) RGB based variable-bits image steganography. In: Proceedings of IEEE Asia pacific services computing conference (ISBN: 978-0-7695-3473-2), pp. 1322–1327. Available at http://www.ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4780862
  18. 18.
    Alseelawi NS, Ismaiel TZ, Sabir FA (2015) High capacity steganography method based upon RGBA image. Int J Adv Res Comput Commun Eng (ISSN: 2278-1021), 4(6). Available at http://www.ijarcce.com/upload/2015/june-15/IJARCCE%2027.pdf
  19. 19.
    Thiyagarajan P, Aghila G (2013) Reversible dynamic secure steganography for medical image using graph coloring. Health Policy Technol 2(3):151–161. Available at http://www.sciencedirect.com/science/article/pii/S2211883713000403
  20. 20.
    Ross J, Anderson F, Petitcolas AP (1998) On the limits of steganography. In: IEEE Journal of selected Areas in communication, Special Issue on Copyright & Privacy protection (ISSN: 0733-8716), 6(4):474–481Google Scholar
  21. 21.
    Swain G, Lenka SK (2012) LSB array based image steganography technique by exploring the four least significant bits. Springer, In: Proceedings of 4th international conference, Obcom 2011, CCIS (ISBN: 978-3-642-29216-3), 2(270):479–488Google Scholar
  22. 22.
    Swain G, Lenka SK (2015) A novel steganography technique by mapping words with LSB array. Int J Signal Imaging Syst Eng Indersci (ISSN: 1748-0701), 8(1–2). Available at: http://www.inderscience.com/link.php?id=67052
  23. 23.
    Wang XZ, Ashfaq RAR, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196MathSciNetCrossRefGoogle Scholar
  24. 24.
    Wang XZ (2015) Uncertainty in learning from big data-editorial. J Intell Fuzzy Syst 28(5):2329–2330CrossRefGoogle Scholar
  25. 25.
    Lu SX, Wang XZ, Zhang GQ, Zhou X (2015) Effective algorithms of the Moore–Penrose inverse matrices for extreme learning machine. Intell Data Anal 19(4):743–760CrossRefGoogle Scholar
  26. 26.
    He YL, Wang XZ, Huang JZX (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci. doi: 10.1016/j.ins.2016.01.037 Google Scholar
  27. 27.
    Ashfaq RAR, Wang XZJ, Huang ZX, Abbas H, He YL (2016) Fuzziness based semi-supervised learning approach for intrusion detection system (IDS). Inf Sci. doi: 10.1016/j.ins.2016.04.019 Google Scholar
  28. 28.
    Anthony JM, Robert NF, Yang L, Nathaniel AW, Steven DB (2004) An introduction to decision tree modeling. J Chemomet 18(6):275–285. Available at http://www.onlinelibrary.wiley.com/doi/10.1002/cem.873/pdf
  29. 29.
    Thomas HC, Charles EL, Ronald LR, Clifford S (2001) Introduction to algorithms, 2nd edn. MIT Press and McGraw-Hill, Section 10.1: Stacks and queues, pp. 200–204. Available at http://www.dcc.ufrj.br/~francisco_vianna/livros/Introduction.To.Algorithms.-.Cormen.-.2nd.Ed.pdf
  30. 30.
    Li B et al (2011) A survey on image steganography and steganalysis. Journal of Information Hiding and Multimedia Signal Processing (ISSN: 2073-4212), 2(2):142–172. Available at http://www.bit.kuas.edu.tw/~jihmsp/2011/vol2/JIH-MSP-2011-03-005.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMody University of Science and TechnologyLakshmangarhIndia

Personalised recommendations