If you find our work helpful for your research, please cite:
@article{li2025dppad,
title={DPPAD-IE: Dynamic Polyhedra Permutating and Arnold Diffusing Medical Image Encryption Using 2D Cross Gaussian Hyperchaotic Map},
author=
732A
{Li, Quanjun and Li, Qian and Ling, Bingo Wing-Kuen and Pun, Chi-Man and Huang, Guoheng and Yuan, Xiaochen and Zhong, Guo and Ayouni, Sarra and Chen, Jianwu},
journal={IEEE Transactions on Consumer Electronics},
year={2025},
publisher={IEEE}
}DPPAD-IE: Dynamic Polyhedra Permutating and Arnold Diffusing Medical Image Encryption Using 2D Cross Gaussian Hyperchaotic Map
Quanjun Li , Qian Li , Bingo Wing-Kuen Ling, Chi-Man Pun, Guoheng Huang, Xiaochen Yuan, Guo Zhong, Sarra Ayouni, and Jianwu Chen
-
Guangdong Province
-
Macau SAR
-
Saudi Arabia
-
Fujian Province
Requirements: Matlab 2024
To ensure robust encryption and evaluation, we first prepare the datasets used in this study. The selected datasets include:
-
Brain Tumors Dataset
- A medical imaging dataset containing brain tumor scans.
- Used for testing encryption robustness in medical image security.
-
Computer-Aided Diagnostic (CAD) Dataset
- A dataset of chest X-ray images for automated disease classification.
- Utilized to evaluate the impact of encryption on deep ensemble learning models.
Once the datasets are prepared, the next step is to apply the encryption algorithm to transform the images.
- Run
encrypt.mto execute the encryption process. - After execution, the encrypted images are generated and stored for further analysis.
- The encrypted outputs can be used for security validation, decryption testing, and performance evaluation.
This encryption process ensures the confidentiality and integrity of sensitive medical images while enabling secure AI-driven diagnostics.
This work was supported by the Key Areas Research and Development Program of Guangzhou (Grant No. 2023B01J0029), the Guangdong Provincial Key Laboratory of Cyber-Physical System (Grant No. 2020B1212060069), and the University of Macau (Grant No. MYRG2022-00190-FST).
Additionally, this research was partially funded by the Science and Technology Development Fund, Macau SAR (Grant No. 0141/2023/RIA2), the Guangdong Basic and Applied Basic Research Foundation (Grant Nos. 2024A1515011729 and 0193/2023/RIA3), and the Researchers Supporting Project (Grant No. RSPD2025R564) from King Saud University, Riyadh, Saudi Arabia.
We sincerely appreciate the generous support from these institutions.