Image Encryption Algorithms Using Machine Learning and Deep Learning Techniques—A Survey

Lakshmi, T. Naga; Jyothi, S.; Kumar, Madapuri Rudra

doi:10.1007/978-3-030-68291-0_40

Cited by 5 publications

(1 citation statement)

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“…With the development of IoMT, many medical images need to be easily distributed among doctors and patients. [1] Therefore, medical image encryption technology is essential to protect the confidentiality and integrity of medical images during storage and transmission, and it should also be able to effectively resist various network attacks, including cropping attacks and noise attacks, ensuring the quality of medical images throughout the transmission process [2]. By employing different encryption algorithms and keys, medical image encryption ensures that only authorized individuals, such as doctors and medical staff, can access the images and use them for diagnosis or treatment, safeguarding sensitive information from unauthorized access or interception.…”

Section: Introductionmentioning

confidence: 99%

A Novel Medical Image Encryption Scheme Based on Deep Learning Feature Encoding and Decoding

Long,

Chen,

Liu

et al. 2024

IEEE Access

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Medical image encryption is essential to protect the privacy and confidentiality of patients' medical records. Deep learning-based encryption, which leverages the nonlinear characteristics of neural networks, has emerged as a promising new method for protecting medical images. In this paper, we present insights into deep learning-based medical image encryption and propose a novel end-to-end medical image encryption scheme based on these insights that leverage feature encoding and decoding for encrypting and decrypting images. Firstly, we explore a method that combines keys generated by the Logistic Map with encoded plaintext image features to improve network diffusion performance. Secondly, we employ a reversible neural network to enhance plaintext image reconstruction while maintaining encryption effectiveness. Finally, we propose a series of novel loss functions to measure the cost with the ideal cryptographic algorithm and continuously optimize our network. Experimental results demonstrate that our scheme improves the performance of image encryption and decryption and resists brute force attacks, statistical attacks, noise and cropping attacks.

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Section: Introductionmentioning

confidence: 99%