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ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Jiang, Donghua; Tsafack, Nestor; Boulila, Wadii; Ahmad, Jawad; Barba-Franco, J.J.

Authors

Donghua Jiang

Nestor Tsafack

Wadii Boulila

J.J. Barba-Franco



Abstract

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

Journal Article Type Article
Acceptance Date Aug 28, 2023
Deposit Date Sep 6, 2023
Publicly Available Date Sep 6, 2023
Print ISSN 0957-4174
Electronic ISSN 1873-6793
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 236
Article Number 121378
DOI https://doi.org/10.1016/j.eswa.2023.121378
Keywords Compressive sensing, adaptive sparse representation, singular value decomposition, chaotic neural network, image encryption

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