arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.05523cs.CRcs.CVeess.IV
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DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things

University of Electronic Science and Technology of China(电子科技大学);Institute of Electronic and Information Engineering of UESTC in Guangdong(电子科技大学广东电子信息工程研究院);National Natural Science Foundation of China(国家自然科学基金委员会)

Yi Ding, Guozheng Wu, Dajiang Chen, Ning Zhang, Linpeng Gong, Mingsheng Cao, Zhiguang Qin

英文摘要:

Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for 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 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. Target domain is regarded as a "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 an 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 interested object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray dataset. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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