# DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things

arXiv: https://arxiv.org/abs/2004.05523 · HTML: https://mlsanity.com/paper/2004.05523/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

## Built on by (most cited first, 11 of 11)

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- [Hiding Visual Information via Obfuscating Adversarial Perturbations](https://mlsanity.com/paper/2209.15304/) (2022)
- [PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning](https://mlsanity.com/paper/2312.10380/) (2023)
- [Unveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding](https://mlsanity.com/paper/2408.04261/) (2024)
- [From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks](https://mlsanity.com/paper/2203.13464/) (2022)
- [Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential](https://mlsanity.com/paper/2501.18834/) (2025)
- [AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective](https://mlsanity.com/paper/2603.24857/) (2026)
- [Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence](https://mlsanity.com/paper/2305.09224/) (2023)
- [Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption](https://mlsanity.com/paper/2407.06570/) (2024)

## Abstract (arXiv)

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.