DeepEDN: A Deep-Learning-Based Image Encryption and Decryption Network for Internet of Medical Things
DeepEDN: A Deep-Learning-Based Image Encryption and Decryption Network for Internet of Medical Things
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DOI：
10.1109/jiot.2020.3012452
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发表时间：
2021-02-01
影响因子：
10.6
通讯作者：
Qin, Zhiguang
中科院分区：
文献类型：
--
作者：
Ding, Yi;Wu, Guozheng;Qin, Zhiguang
医疗物联网（IoMT）可以将许多医学影像设备连接到医疗信息网络，以方便医生的诊断和治疗过程。由于医学图像包含敏感信息，因此保护患者的隐私或安全具有重要意义，但也极具挑战性。在这项工作中，提出了一种基于深度学习的图像加密和解密网络（DeepEDN），以完成对医学图像的加密和解密过程。具体来说，在DeepEDN中，循环生成对抗网络（Cycle-GAN）被用作主要学习网络，将医学图像从其原始域转移到目标域。目标域被视为“隐藏因素”，以指导学习模型实现加密。通过重构网络将加密后的图像恢复为原始（明文）图像，实现图像解密。为了方便直接从隐私保护环境中进行数据挖掘，提出了一种感兴趣区域（ROI）挖掘网络，从加密图像中提取感兴趣的对象。在胸部X射线数据集上评估所提出的DeepEDN。大量的实验结果和安全性分析表明，该方法可以实现高水平的安全性和良好的性能在效率。
Internet of Medical Things (IoMT) can connect many medical imaging equipment to the medical information network to facilitate the process of diagnosing and treating doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep-learning-based image encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the cycle-generative adversarial network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. The target domain is regarded as "hidden factors" to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest (ROI)-mining network is proposed to extract the interesting object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray data set. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.