论文标题
DeepKeygen：用于医学图像加密和解密的基于深度学习的流密码生成器
DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption
论文作者
论文摘要
对医疗图像加密的需求越来越明显，例如保护患者医学成像数据的隐私。在本文中，提出了一个新颖的基于深度学习的密钥生成网络（DeepKeygen）作为流密码生成器来生成私钥，然后可以将其用于对医学图像的加密和解密。在DeepKeygen中，使用生成对抗网络（GAN）作为生成私钥的学习网络。此外，转换域（代表要生成的私钥的“样式”）旨在指导学习网络实现私钥生成过程。 DeepKeygen的目的是了解如何将初始图像传输到私钥的映射关系。我们使用三个数据集评估了DeepKeygen，即：Montgomery County Chest X射线数据集，超声波臂式丛集数据集和BRATS18数据集。评估发现和安全性分析表明，提出的密钥生成网络可以在生成私钥时获得高级安全性。
The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients' medical imaging data. In this paper, 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 datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.
