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Open Access

ARTICLE

AI-Powered Image Security: Utilizing Autoencoders for Advanced Medical Image Encryption

Fehaid Alqahtani*
Department of Computer Science, King Fahad Naval Academy, Al Jubail, 35512, Saudi Arabia
* Corresponding Author: Fehaid Alqahtani. Email: email
(This article belongs to the Special Issue: Emerging Technologies in Information Security )

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.054976

Received 13 June 2024; Accepted 15 August 2024; Published online 11 September 2024

Abstract

With the rapid advancement in artificial intelligence (AI) and its application in the Internet of Things (IoT), intelligent technologies are being introduced in the medical field, giving rise to smart healthcare systems. The medical imaging data contains sensitive information, which can easily be stolen or tampered with, necessitating secure encryption schemes designed specifically to protect these images. This paper introduces an artificial intelligence-driven novel encryption scheme tailored for the secure transmission and storage of high-resolution medical images. The proposed scheme utilizes an artificial intelligence-based autoencoder to compress high-resolution medical images and to facilitate fast encryption and decryption. The proposed autoencoder retains important diagnostic information even after reducing the image dimensions. The low-resolution images then undergo a four-stage encryption process. The first two encryption stages involve permutation and the next two stages involve confusion. The first two stages ensure the disruption of the structure of the image, making it secure against statistical attacks. Whereas the two stages of confusion ensure the effective concealment of the pixel values making it difficult to decrypt without secret keys. This encrypted image is then safe for storage or transmission. The proposed scheme has been extensively evaluated against various attacks and statistical security parameters confirming its effectiveness in securing medical image data.

Keywords

Artificial Intelligence; image encryption; chaos; medical image encryption

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