A K-SVD Based Compressive Sensing Method for Visual Chaotic Image Encryption

Zizhao Xie, Jingru Sun (), Yiping Tang, Xin Tang, Oluyomi Simpson and Yichuang Sun
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Zizhao Xie: School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
Jingru Sun: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Yiping Tang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Xin Tang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Oluyomi Simpson: School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Yichuang Sun: School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

Mathematics, 2023, vol. 11, issue 7, 1-20

Abstract: The visually secure image encryption scheme is an effective image encryption method, which embeds an encrypted image into a visual image to realize a secure and secret image transfer. This paper proposes a merging compression and encryption chaos image visual encryption scheme. First, a dictionary matrix D is constructed with the plain image by the K-SVD algorithm, which can encrypt the image while sparsing. Second, an improved Zeraoulia-Sprott chaotic map and logistic map are employed to generate three S-Boxes, which are used to complete scrambling, diffusion, and embedding operations. The secret keys of this scheme contain the initial value of the chaotic system and the dictionary matrix D, which significantly increases the key space, plain image correlation, and system security. Simulation shows the proposed image encryption scheme can resist most attacks and, compared with the existing scheme, the proposed scheme has a larger key space, higher plain image correlation, and better image restoration quality, improving image encryption processing efficiency and security.

Keywords: image encryption; compressive sensing; K-SVD; chaos (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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