Novel Technique for Secure Medical Image Transmission

Authors

  • Anant Shankar Ellapalli Dept. of ECE, S.V. University College of Engineering, S V University, Tirupati –517502, India
  • S. Varadarajan Dept. of ECE, S.V. University College of Engineering, S V University, Tirupati –517502, India

Keywords:

Adaptively Scanned Wavelet Difference Reduction (ASWDR), ASCII-based Encryption (ASCIE), Cryptography, Image quality assessment metrics Compression, Lifting Wavelet Transform (LWT), Steganography

Abstract

The quest to develop cutting-edge security mechanisms is increasing due to digitalization, the development of the Internet, and the necessity of confidential communication in every industry governed by the Internet. Advanced declaration, computerized mark, and encryption are only a few of the methods employed to address these security concerns. Yet these approaches can't get you far in negotiations. To address these concerns this work proposes a hybrid model that utilizes both steganography and encryption to provide dual security to the data. Accordingly, to embed large quantities of data, Adaptively Scanned Wavelet Difference Reduction model is utilized to compress the secret image initially followed by ASCII-based encryption and embedding utilizing Lifting wavelet transform (LWT) coefficients. This work mainly concentrates on hiding the analyzed covid-19 chest X-rays utilizing the introduced hybrid model. To validate the proposed model, several experiments were conducted and obtained various image quality assessment metrics. From the relative analysis, it was evident that the proposal outperforms its peers.

Downloads

Download data is not yet available.

References

Rahman, S., Uddin, J., Zakarya, M., Hussain, H., Khan, A. A., Ahmed, A., & Haleem, M. (2023). A Comprehensive Study of Digital Image Steganographic Techniques. IEEE Access.

Anderson, R. J., & Petitcolas, F. A. (1998). On the limits of steganography. IEEE Journal on selected areas in communications, 16(4), 474-481.

R. R. Hake, (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. Am. J. Phys., vol. 66, no. 1, pp. 64–74.

Zielińska, E., Mazurczyk, W., & Szczypiorski, K. (2014). Trends in steganography. Communications of the ACM, 57(3), 86-95.

Feng, B., Lu, W., & Sun, W. (2014). Secure binary image steganography based on minimizing the distortion on the texture. IEEE transactions on Information Forensics and Security, 10(2), 243-255.

Al-Otaibi, N. A., & Gutub, A. A. (2014, December). Flexible stego-system for hiding text in images of personal computers based on user security priority. In Proceedings of 2014 international conference on advanced engineering technologies (AET-2014) (pp. 250-256).

Guo, L., Ni, J., Su, W., Tang, C., & Shi, Y. Q. (2015). Using statistical image model for JPEG steganography: Uniform embedding revisited. IEEE Transactions on Information Forensics and Security, 10(12), 2669-2680.

Abikoye, O. C., Ojo, U. A., Awotunde, J. B., & Ogundokun, R. O. (2020). A safe and secured iris template using steganography and cryptography. Multimedia Tools and Applications, 79, 23483-23506.

Hureib, E. S., & Gutub, A. A. (2020). Enhancing medical data security via combining elliptic curve cryptography and image steganography. Int. J. Comput. Sci. Netw. Secur.(IJCSNS), 20(8), 1-8.

Seshadri, R., & Trivedi, T. R. (2010). Generate a key for MAC Algorithm using Biometric Fingerprint. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), 1(4), 38-45.

Naz, F., Shoukat, I. A., Ashraf, R., Iqbal, U., & Rauf, A. (2020). An ASCII based effective and multi-operation image encryption method. Multimedia Tools and Applications, 79, 22107-22129.

Nasiri, H., & Hasani, S. (2022). Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography, 28(3), 732-738.

Mentzer, F., Toderici, G. D., Tschannen, M., & Agustsson, E. (2020). High-fidelity generative image compression. Advances in Neural Information Processing Systems, 33, 11913-11924.

Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2020). Learned image compression with discretized gaussian mixture likelihoods and attention modules. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7939-7948).

Starosolski, R. (2020). Hybrid adaptive lossless image compression based on discrete wavelet transform. Entropy, 22(7), 751.

Karakus, S., & Avci, E. (2020). A new image steganography method with optimum pixel similarity for data hiding in medical images. Medical Hypotheses, 139, 109691.

Hamza, A., Shehzad, D., Sarfraz, M. S., Habib, U., & Shafi, N. (2021). Novel secure hybrid image steganography technique based on pattern matching. KSII transactions on internet and information systems (TIIS), 15(3), 1051-1077.

Almawgani, A. H. M., Alhawari, A. R., Hindi, A. T., Al-Arashi, W. H., & Al-Ashwal, A. Y. (2022). Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data. Multidimensional systems and signal processing, 1-18.

Lim, S. J. (2022). Hybrid image embedding technique using Steganographic Signcryption and IWT-GWO methods. Microprocessors and Microsystems, 95, 104688.

Abd-El-Atty, B. (2023). A robust medical image steganography approach based on particle swarm optimization algorithm and quantum walks. Neural Computing and Applications, 35(1), 773-785.

Mehta, R., Gupta, K., & Yadav, A. K. (2020). An adaptive framework to image watermarking based on the twin support vector regression and genetic algorithm in lifting wavelet transform domain. Multimedia Tools and Applications, 79, 18657-18678.

Walker, J. S., & Nguyen, T. Q. (2000, September). Adaptive scanning methods for wavelet difference reduction in lossy image compression. In Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101) (Vol. 3, pp. 182-185). IEEE.

Kim, B. J., & Pearlman, W. A. (1997, March). An embedded wavelet video coder using three-dimensional set partitioning in hierarchical trees (SPIHT). In Proceedings DCC'97. Data Compression Conference (pp. 251-260). IEEE.

Sudhakar, R., Karthiga, R., & Jayaraman, S. (2005). Image compression using coding of wavelet coefficients–a survey. ICGST-GVIP Journal, 5(6), 25-38.

Tian, J., & Wells Jr, R. O. (1996, April). A Lossy Image Codec Based on Index Coding. In Data compression conference (p. 456).

Shanmugasundaram, S., & Lourdusamy, R. (2011). A comparative study of text compression algorithms. International Journal of Wisdom Based Computing, 1(3), 68-76.

Jacob, N., Somvanshi, P., & Tornekar, R. (2012). Comparative analysis of lossless text compression techniques. International Journal of Computer Applications, 56(3).

Valens, C. (1999). The fast lifting wavelet transform. In The Math Forum (pp. 10-12).

Bhardwaj, A., Wadhwa, A., & Verma, V. S. (2019, January). Image enhancement in lifting wavelet transform domain. In AIP Conference Proceedings (Vol. 2061, No. 1, p. 020027). AIP Publishing LLC.

Majead Kadum, T. A., & Al-Saad, S. N. (2016). Image Hiding Using Lifting Wavelet Transform. International Journal of Scientific & Engineering Research, 7.

Irawati, I. D., Hadiyoso, S., Budiman, G., & Mulyana, A. (2020). Lifting Wavelet Transform in Compressive Sensing for MRI Reconstruction. Journal of Southwest Jiaotong University, 55(5).

Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in biology and medicine, 121, 103792.

Karakış, R., Güler, I., Çapraz, İ., & Bilir, E. (2015). Medikal Sinyaller için Benzerlik Tabanlı Görüntü Steganografi Uygulaması. In Akademik Bilişim. Kabul Edildik.

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 & Information Technology, 7(4), 79-91.

Iqbal, U. (2021). Hybrid Image Steganography Method with Random Embedding of Encrypted Message. Lahore Garrison University Research Journal of Computer Science and Information Technology, 5(4), 36-49.

Downloads

Published

30.11.2023

How to Cite

Ellapalli, A. S. ., & Varadarajan, S. . (2023). Novel Technique for Secure Medical Image Transmission. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 56–63. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3938

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.