Title
DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption
Abstract
The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the “style” of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.
Year
DOI
Venue
2022
10.1109/TNNLS.2021.3062754
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Deep Learning,Humans,Image Processing, Computer-Assisted,Neural Networks, Computer
Journal
33
Issue
ISSN
Citations 
9
2162-237X
0
PageRank 
References 
Authors
0.34
14
6
Name
Order
Citations
PageRank
Yi Ding110037.68
Fuyuan Tan200.34
Zhen Qin3254.47
Mingsheng Cao400.68
Kim-Kwang Raymond Choo54103362.49
Zhiguang Qin632163.02