LSB steganography strengthen footprint biometric template

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.225371

Keywords:

steganography, foot-tip template, hybridization, stego image, cover image, clustering, biometrics

Abstract

Steganography is the science of hiding secret data inside another data type as image and text. This data is known as carrier data; it lets people interconnect secretly. This suggested paper aims to design a Steganography Biometric Imaging System (SBIS). The system is constructed in a hybridization manner between image processing, steganography, and artificial intelligence techniques. During image processing techniques the system receives RGB foot-tip images and preprocesses the images to get foot-template images. Then a chain code is illustrated for personal information within the foot-template image by Least Significant Bit (LSB). Accurate recognition operation is performed by artificial bee colony optimization (ABC). The automated system was tested on a live-took about ninety RGB foot-tip images known as the cover image and clustered to nine clusters that authorized visual database. The Least Significant Bit method transforms the foot template to a stego image and is stored on a stego visual database for further use. Features database was constructed for each stego footprint template. This step converts the image to quantities data and stored in an Excel feature database file. The quantities data was used at the recognition stage to produce either a notification of rejection or acceptance. At the acceptance choice, the corresponding stego foot-tip template occurrence was retrieved, it is corresponding individual data were extracted and cluster position on the stego template visual database. Indeed, the foot-tip template is displayed. The suggested work consequence is affected by the optimum feature selection via the artificial bee colony optimization usage and clustering, which declined the complication and subsequently raised the recognition rate to 93.65 %. This rate competes out the technique over others’ techniques in the field of biometric recognition

Author Biography

Israa Mohammed Khudher, University of Mosul

PhD, Assistant Professor, Head of Department

Department of Computer Science

College of Education for Pure Sciences

References

  1. Nagwanshi, K. K. (2019). Cyber-Forensic Review of Human Footprint and Gait for Personal Identification. IAENG International Journal of Computer Science, 46 (4), 645–661.
  2. McAteer, I., Ibrahim, A., Zheng, G., Yang, W., Valli, C. (2019). Integration of biometrics and steganography: A comprehensive review. Technologies, 7 (2), 34. doi: https://doi.org/10.3390/technologies7020034
  3. Kant, C., Nath, R., Chaudhary, S. (2008). Biometrics security using steganography. International Journal of Security, 2 (1), 1–5. Available at: https://www.cscjournals.org/manuscript/Journals/IJS/Volume2/Issue1/IJS-5.pdf
  4. Johnson, N. F., Jajodia, S. (1998). Steganalysis of Images Created Using Current Steganography Software. Lecture Notes in Computer Science, 273–289. doi: https://doi.org/10.1007/3-540-49380-8_19
  5. Chandran, S., Bhattacharyya, K. (2015). Performance analysis of LSB, DCT, and DWT for digital watermarking application using steganography. 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). doi: https://doi.org/10.1109/eesco.2015.7253657
  6. Khokher, R., Chandra Singh, R. (2016). Footprint-Based Personal Recognition using Scanning Technique. Indian Journal of Science and Technology, 9 (44). doi: https://doi.org/10.17485/ijst/2016/v9i44/105167
  7. Ye, H., Kobashi, S., Hata, Y., Taniguchi, K., Asari, K. (2009). Biometric System by Foot Pressure Change Based on Neural Network. 2009 39th International Symposium on Multiple-Valued Logic. doi: https://doi.org/10.1109/ismvl.2009.16
  8. Yun, J., Abowd, G., Woo, W., Ryu, J. (2007). Biometric User Identification with Dynamic Footprint. 2007 Second International Conference on Bio-Inspired Computing: Theories and Applications. doi: https://doi.org/10.1109/bicta.2007.4806456
  9. Hashem, K. M., Ghali, F. (2016). Human Identification Using Foot Features. International Journal of Engineering and Manufacturing, 6 (4), 22–31. doi: https://doi.org/10.5815/ijem.2016.04.03
  10. Douglas, M., Bailey, K., Leeney, M., Curran, K. (2017). An overview of steganography techniques applied to the protection of biometric data. Multimedia Tools and Applications, 77 (13), 17333–17373. doi: https://doi.org/10.1007/s11042-017-5308-3
  11. Keatsamarn, T., Visitsattapongse, S., Pintavirooj, C. (2020). Footprint Pressure-Based Personal Recognition. International Journal of Pharma Medicine and Biological Sciences, 9 (2), 65–69. doi: https://doi.org/10.18178/ijpmbs.9.2.65-69
  12. Nagwanshi, K. K., Dubey, S. (2018). Mathematical Modeling of Footprint Based Biometric Recognition. International Journal of Mathematics Trends and Technology, 54 (6), 500–507. doi: https://doi.org/10.14445/22315373/ijmtt-v54p560
  13. Ibrahim, Y. I., Alhamdani, I. M. (2019). A hyprid technique for human footprint recognition. International Journal of Electrical and Computer Engineering (IJECE), 9 (5), 4060–4068. doi: https://doi.org/10.11591/ijece.v9i5.pp4060-4068
  14. Alhamdani, I. M., Ibrahim, Y. I. (2020). Swarm intelligent hyperdization biometric. Indonesian Journal of Electrical Engineering and Computer Science, 18 (1), 385. doi: https://doi.org/10.11591/ijeecs.v18.i1.pp385-395
  15. Kaur, N. I., Kaur, A. (2017). Art of Steganography. International Journal of Advanced Trends in Computer Applications (IJATCA), 4 (2), 30–33.
  16. Ali, U. A. M. E., Sohrawordi, M., Uddin, M. P. (2019). A Robust and Secured Image Steganography using LSB and Random Bit Substitution. American Journal of Engineering Research (AJER), 8 (2), 39–44.‏
  17. Mousa, S. M. A. (2017). LSBs Steganography Based on R-Indicator. The Islamic University Gaza, 73. Available at: https://iugspace.iugaza.edu.ps/bitstream/handle/20.500.12358/20075/file_1.pdf?sequence=1&isAllowed=y
  18. Cheddad, A. (2009). Steganoflage: A New Image Steganography Algorithm. School of Computing & Intelligent Systems Faculty of Computing & Engineering, University of Ulster. Available at: https://theses.eurasip.org/media/theses/documents/cheddad-abbas-steganoflage-a-new-image-steganography-algorithm_1.pdf
  19. Awadh, W. A., Hashim, A. S., Hamoud, A. K. (2019). A Review of Various Steganography Techniques in Cloud Computing. University of Thi-Qar Journal of Science, 7 (1), 113–119. doi: https://doi.org/10.32792/utq/utjsci/vol7/1/19
  20. Hussain, Me., Hussain, Mu. (2013). A survey of image steganography techniques. International Journal of Advanced Science and Technology, 54, 113–124.
  21. Chitradevi, B., Thinaharan, N., Vasanthi, M. (2017). Data Hiding Using Least Significant Bit Steganography in Digital Images. Statistical Approaches on Multidisciplinary Research, 143–150. Available at: https://zenodo.org/record/262996#.YCEyjHQzaUk
  22. Kumar, A., Kumar, D., Jarial, S. K. (2017). A review on artificial bee colony algorithms and their applications to data clustering. Cybernetics and Information Technologies, 17 (3), 3–28. doi: https://doi.org/10.1515/cait-2017-0027
  23. Baji, F., Mocanu, M. (2018). Chain Code Approach for Shape based Image Retrieval. Indian Journal of Science and Technology, 11 (3). doi: https://doi.org/10.17485/ijst/2018/v11i3/119998
  24. Salem, A.-B. M., Sewisy, A. A., Elyan, U. A. (2005). A vertex chain code approach for image recognition. International Journal on Graphics, vision and Image processing, 5 (3).‏
  25. Govindaraju, V., Shi, Z., Schneider, J. (2003). Feature Extraction Using a Chaincoded Contour Representation of Fingerprint Images. Audio- and Video-Based Biometric Person Authentication, 268–275. doi: https://doi.org/10.1007/3-540-44887-X_32
  26. Al-Najjar, Y. A. Y., Soong, D. C. (2012). Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific & Engineering Research, 3 (3).
  27. Gonzalez, R. C., Woods, R. E. (2002). Digital image processing. Prentice-Hall, 793.
  28. Ambeth Kumar, V. D., Ramakrishnan, M. (2010). Footprint recognition using modified sequential haar energy transform (MSHET). IJCSI International Journal of Computer Science, 7 (3), 47–51.
  29. De Oliveira, I. O., Laroca, R., Menotti, D., Fonseca, K. V. O., Minetto, R. (2019). Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks. arXiv.org. Available at: https://arxiv.org/pdf/1911.05541.pdf
  30. Rusdi, N., Yahya, Z. R., Roslan, N., Azman, W. Z. (2018). Reconstruction of medical images using artificial bee colony algorithm. Mathematical Problems in Engineering. doi: https://doi.org/10.1155/2018/8024762
  31. Abuqadumah, M. M. A., Ali, M. A. M., Almisreb, A. A., Durakovic, B. (2019). Deep transfer learning for human identification based on footprint: a comparative study. Periodicals of Engineering and Natural Scinces, 7 (3), 1300–1307. doi: http://dx.doi.org/10.21533/pen.v7i3.733

Downloads

Published

2021-02-27

How to Cite

Khudher, I. M. (2021). LSB steganography strengthen footprint biometric template . Eastern-European Journal of Enterprise Technologies, 1(9 (109), 58–65. https://doi.org/10.15587/1729-4061.2021.225371

Issue

Vol. 1 No. 9 (109) (2021): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2021 Israa Mohammed Khudher

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.