Cryptography of medical images based on a combination between chaotic and neural network
Abstract
This study presents a novel chaotic–neural network of image encryption and decryption image applied to the domain of medical. The main objective behind the proposed technique is to ensure the safety of medical images with a less complex algorithm compared with the existing methods. In order to improve the robustness, the totality of the pixels related to the host image is XORed with a generation key. After that, with a chaotic system (logistic map), the binary sequence is generated in order to set the weights w ij and bias bi of neuron network with the goal of encrypting the pixels issued from the previous step. Simulation and experiments were carried out on medical images coded on 8 and 12 bits/pixel. The obtained results confirmed the performance and the efficiency of the proposed method, which is compliant with Digital Imaging and Communications in Medicine standards.
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The SSIM of Dridi et al.'s approach is better than that of Wang et al.'s approach.
“However, its SSIM is better than that of Wang et.al. [54].”
From A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM (2024) · cites this paper as [54]
The work of Wang et al. uses a chaotic encryption principle that focuses on synchronized outcome, and hence its algorithm consumes slightly more time.
“The work carried out by Wang et.al. has used chaotic encryption principle which offers more focus on synchronized outcome and hence slightly more time is consumed by its algorithm [54].”
From A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM (2024) · cites this paper as [54]
Cites
A novel image encryption/decryption scheme based on chaotic neural networks
Bigdeli, N., Farid, Y., & Afshar, K. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.01.007 · Cited by 88 · see citing works
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An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems
Luo, Y., Ouyang, X., Liu, J., & Cao, L. (2019) · IEEE Access · doi:10.1109/access.2019.2906052 · Cited by 172 · see citing works
A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM
Panigrahy, A. K., Maniyath, S. R., Sathiyanarayanan, M., Dholvan, M., Ramaswamy, T., Hanumanthakari, S., Vignesh, N. A., Kanithan, S., & Swain, R. (2024) · IEEE Access · doi:10.1109/access.2024.3353294 · Cited by 35 · see citing works
- Authors
- Dridi, M., Hajjaji, M. A., Bouallegue, B., & Mtibaa, A.
- Affiliations
- University of Monastir (TN)
- Laboratory of Electronics and Micro‐electronics University of Monastir, Faculty of Sciences of Monastir, University of Monastir Tunisia
- Corresponding author
- Dridi, Manel
- Venue
- IET Image Processing
- Published
- 2016-11
- Volume
- 10
- Issue
- 11
- Pages
- 830-839
- ISSN
- 1751-9659 (ISSN-L)
- 1751-9667
- OpenAlex
- W2475191673
- Cited by
- 71
- References
- 1
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 9 · 2022: 14 · 2021: 9 · 2020: 10 · 2019: 8 · 2018: 5 · 2017: 1 · 2016: 1
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IET Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- medical image encryption, chaotic neural networks, logistic map, image decryption
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