Strengthening steganoghraphy by using crow search algorithm of fingerprint image

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.200282

Keywords:

information hiding, steganography, CSA, cover image, stego-image, meta-heuristic, PSNR, MSE, NPCR, SSIM, CC

Abstract

In image steganography, secret communication is implemented to hide secret information into the cover image (used as the carrier to embed secret information) and generate a stego-image (generated image carrying hidden secret information).

Nature provides many ideas for computer scientists. One of these ideas is the orderly way in which the organisms work in nature when they are in groups. If the group itself is treated as an individual (the swarm), the swarm is more intelligent than any individual in the group. Crow Search Algorithm (CSA) is a meta-heuristic optimizer where individuals emulate the intelligent behavior in a group of crows. It is based on simulating the intelligent behavior of crow flocks and attempts to imitate the social intelligence of a crow flock in their food gathering process.

 This paper presents a novel meta-heuristic approach based on the Crow Search Algorithm (CSA), where at the beginning the color cover image is converted into three channels (RGB) and then those channels are converted into three spaces, which are Y, Cb, Cr. After applying Discrete wavelet transform (DWT) on each space separately, the CSA algorithm is used on each space (YCbCr) to find the best location that will be used to hide secret information, the CSA is used to increase the security force by finding the best locations that have high frequency and are invulnerable to attacks, the DWT is used to increase robustness against noise. The proposed system is implemented on three fingerprint cover images for experiments, for the quality of stego image the histogram, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate Test (NPCR), Structural Similarity Index Metric (SSIM) and Correlation Coefficients (CC) are computed. The result demonstrated the strength of the CSA to hide data, also discovered that using CSA may lead to finding favorable results compared to the other algorithms

Author Biography

Omar Younis Abdulhammed, University of Garmian Kalar, Sulaimani, Kurdistan Region, Iraq

PhD in computer science, Assistant Professor

Department of Computer Science

College of Science

References

  1. Nguyen, T. D., Le, D. H. (2020). A secure image steganography based on JND model. International Journal of Electrical and Computer Engineering (IJECE), 10 (2), 2088. doi: https://doi.org/10.11591/ijece.v10i2.pp2088-2096
  2. Maleki, N., Jalali, M., Jahan, M. V. (2014). Adaptive and non-adaptive data hiding methods for grayscale images based on modulus function. Egyptian Informatics Journal, 15 (2), 115–127. doi: https://doi.org/10.1016/j.eij.2014.06.001
  3. Al-Ta’i, Z. T. M., Al-Hameed, O. Y. A. (2013). Comparison between PSO and Firefly Algorithms in Fingerprint Authentication. International Journal of Engineering and Innovative Technology (IJEIT), 3 (1), 421–425.
  4. Sun, J., Lai, C.-H., Wu, X.-J. (2012). Particle Swarm Optimisation Classical and Quantum Perspectives. CRC Press.
  5. Sumathi, C. P., Santanam, T., Umamaheswari, G. (2013). A Study of Various Steganographic Techniques Used for Information Hiding. International Journal of Computer Science & Engineering Survey, 4 (6), 9–25. doi: https://doi.org/10.5121/ijcses.2013.4602
  6. Kavitha, Mrs., Kadam, K., Koshti, A., Dungha, P. (2012). Steganography Using Least Signicant Bit Algorithm. Engineering Research and Applications (IJERA), 2 (3), 338–341.
  7. Ghasemi, E., Shanbehzadeh, J., Fassihi, N. (2011). High Capacity Image Steganography Using Wavelet Transform and Genetic Algorithm. Proceeding of the international Multi conference of engineer and computer scientists. Vol. I. Hong Kong.
  8. Vanitha, T., Souza, A. D., Rashmi, B., Dsouza, S. (2014). A Review on Steganography – Least Significant Bit Algorithm and Discrete Wavelet Transform Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 2 (5).
  9. Gulve, A. K., Joshi, M. S. (2015). An Image Steganography Method Hiding Secret Data into Coefficients of Integer Wavelet Transform Using Pixel Value Differencing Approach. Mathematical Problems in Engineering, 2015, 1–11. doi: https://doi.org/10.1155/2015/684824
  10. Klim, S. M. (2017). Selected Least Significant Bit Approach for Hiding Information Inside Color Image Steganography by using Magic Square, 21 (01), 74–88. Available at: https://www.iasj.net/iasj?func=fulltext&aId=130104
  11. Singhal, A. (2017). Steganography Based on Local Binary Pattern and Discrete Wavele t transformation. International Journal of Computer Science Issues, 14 (1), 119–124. doi: https://doi.org/10.20943/01201701.119124
  12. Joshi, K., Gill, S., Yadav, R. (2018). A New Method of Image Steganography Using 7th Bit of a Pixel as Indicator by Introducing the Successive Temporary Pixel in the Gray Scale Image. Journal of Computer Networks and Communications, 2018, 1–10. doi: https://doi.org/10.1155/2018/9475142
  13. I. Mohammed Ali, S., Ghazi Ali, M., Abd Zaid Qudr, L. (2019). PDA: A Private Domains Approach for Improved MSB Steganography Image. Periodicals of Engineering and Natural Sciences (PEN), 7 (3), 1405. doi: https://doi.org/10.21533/pen.v7i3.776
  14. Lakshmi, M., Ramesh Kumar, A. 2018). Optimal Reactive Power Dispatch using Crow Search Algorithm. International Journal of Electrical and Computer Engineering (IJECE), 8 (3), 1423. doi: https://doi.org/10.11591/ijece.v8i3.pp1423-1431
  15. Al-Ta’i, Z. T. M. (2011). Development of Multilayer New Covert AudioCryptographic Model. International Journal of Machine Learning and Computing, 1 (2), 125–131. doi: https://doi.org/10.7763/ijmlc.2011.v1.19
  16. Johnson, N. F., Duric, Z., Jajodia, S. (2001). Information Hiding: Steganography and Watermarking-Attacks and Countermeasures. Springer. doi: https://doi.org/10.1007/978-1-4615-4375-6
  17. Fridrich, J., Goljan, M., Hogea, D., Soukal, D. (2003). Quantitative steganalysis of digital images: estimating the secret message length. Multimedia Systems, 9 (3), 288–302. doi: https://doi.org/10.1007/s00530-003-0100-9
  18. Cox, I. J., Miller, M. L., Bloom, J. A., Fridrich, J., Kalker, T. (2008). Digital Watermarking and Steganography. Elsevier. doi: https://doi.org/10.1016/b978-0-12-372585-1.x5001-3
  19. Atawneh, S., Sumari, P. (2015). An Overview of Frequency-based Digital Image Steganography. International Journal of Cryptology Research, 5 (2), 15–27.
  20. Jayapragash, K., Vijayakumar, P. (2014). Wavelets Transform Based Data Hiding Technique for Stegnography. International Journal of Advanced Research in Computer Engineering & Technology, 3 (4), 1414–1419.
  21. Pavani, M., Naganjaneyulu, S., Nagaraju, C. (2013). A Survey on LSB Based Steganography Methods. International Journal Of Engineering And Computer Science.
  22. Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12. doi: https://doi.org/10.1016/j.compstruc.2016.03.001
  23. Zolghadr-Asli, B., Bozorg-Haddad, O., Chu, X. (2017). Crow Search Algorithm (CSA). Studies in Computational Intelligence. Springer, 143–149. doi: https://doi.org/10.1007/978-981-10-5221-7_14
  24. Rajendran, S., Doraipandian, M. (2017). Chaotic map based random image steganography using LSB technique. International Journal of Network Security, 19, 593–598. doi: http://doi.org/10.6633/IJNS.201707.19(4).12
  25. Yalman, Y., Akar, F., Erturk, I. (2010). An Image Interpolation Based Reversible Data Hiding Method Using R-Weighted Coding. 2010 13th IEEE International Conference on Computational Science and Engineering. doi: https://doi.org/10.1109/cse.2010.52
  26. Akinola, S. O., Olatidoye, A. A. (2015). On the Image Quality and Encoding Times of LSB, MSB and Combined LSB-MSB Steganography Algorithms Using Digital Images. International Journal of Computer Science and Information Technology, 7 (4), 79–91. doi: https://doi.org/10.5121/ijcsit.2015.7407
  27. Pareek, N. K. (2012). Design and Analysis of a Novel Digital Image Encryption Scheme. International Journal of Network Security & Its Applications, 4 (2), 95–108. doi: https://doi.org/10.5121/ijnsa.2012.4207
  28. Younus, Z. S., Hussain, M. K. (2019). Image steganography using exploiting modification direction for compressed encrypted data. Journal of King Saud University - Computer and Information Sciences. doi: https://doi.org/10.1016/j.jksuci.2019.04.008
  29. Kaur, A., Kaur, L., Gupta, S. (2012). Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment. International Journal of Computer Applications, 59 (5), 32–39. doi: https://doi.org/10.5120/9546-3999
  30. Jen, E. K., Johnston, R. G. The Ineffectiveness of Correlation Coefficient for Image Comparisons. Research Paper prepared by Vulnerability Assessment Team, Los Alamos National Laboratory, New Mexico.

Downloads

Published

2020-04-30

How to Cite

Abdulhammed, O. Y. (2020). Strengthening steganoghraphy by using crow search algorithm of fingerprint image. Eastern-European Journal of Enterprise Technologies, 2(9 (104), 28–36. https://doi.org/10.15587/1729-4061.2020.200282

Issue

Vol. 2 No. 9 (104) (2020): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2020 Omar Younis Abdulhammed

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.