{"title":"Medical image encryption algorithm based on Fresnel zone formula, differential neural networks, and pixel-guided perturbation techniques","authors":"","doi":"10.1016/j.compeleceng.2024.109722","DOIUrl":null,"url":null,"abstract":"<div><div>This paper proposes an image encryption technique that integrates deep pixel substitution, data-dependent and chaotic pixel perturbation, and differential neural networks. The pixels of the image are manipulated using a deep pixel substitution operation that is based on the Fresnel Zone equation to eliminate the correlations to the input plain image. Additionally, a perturbation process based on pixel values and chaotic noise is applied to further scramble the image. The resulting image is then subjected to a second round of deep substitution. The differential neural network generates blurring codes by incorporating plain pixel blocks and an encryption key, which are subsequently added to the processed image to produce the final ciphered image. The proposed technique’s effectiveness was evaluated on a large dataset that included both medical and non-medical images. Simulation results indicated that the proposed technique was not only efficient but also effective for both medical and non-medical images, and it outperformed state-of-the-art encryption methods in both security properties and computational efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624006499","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes an image encryption technique that integrates deep pixel substitution, data-dependent and chaotic pixel perturbation, and differential neural networks. The pixels of the image are manipulated using a deep pixel substitution operation that is based on the Fresnel Zone equation to eliminate the correlations to the input plain image. Additionally, a perturbation process based on pixel values and chaotic noise is applied to further scramble the image. The resulting image is then subjected to a second round of deep substitution. The differential neural network generates blurring codes by incorporating plain pixel blocks and an encryption key, which are subsequently added to the processed image to produce the final ciphered image. The proposed technique’s effectiveness was evaluated on a large dataset that included both medical and non-medical images. Simulation results indicated that the proposed technique was not only efficient but also effective for both medical and non-medical images, and it outperformed state-of-the-art encryption methods in both security properties and computational efficiency.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.