A visually secure image encryption scheme using adaptive- thresholding sparsification compression sensing model and newly-designed memristive chaotic map

Aiming at the deficiencies of existing image compression-encryption algorithms, such as unstable reconstruction performance and weak crypticity, a novel visually secure image encryption scheme based on the adaptive-thresholding sparsification compression sensing model and the newly designed memristive chaotic map is proposed in this paper. First, the plain image is sparsely represented in the wavelet packet domain. Then, the coefficient matrix is scrambled and compressed using parallel compressive sensing with assistance from the newly developed adaptive-thresholding sparsification model. Next, the compressed image is re-encrypted by the FAN transform and diffusion to generate a noise like secret image. Finally, the reversible matrix encoding-based embedding method is employed to create a meaningful cipher image. To improve the execution efficiency of the encryption algorithm without affecting the security performance, a low-dimensional discrete memristive chaotic map is designed and applied in the compression, encryption and embedding stages. Additionally, the Lp matrix norm is utilized to establish an adaptive threshold model to effectively enhance the compression performance. Simulation results and analyses are presented to demonstrate the effectiveness, the compressibility, and the secrecy of the proposed scheme.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a visually secure image encryption scheme based on the adaptive-thresholding sparsification compression sensing model and a newly designed memristive chaotic map. The scheme achieves secure and reversible image encryption through steps such as sparse representation, scrambling and compression, encryption, and embedding. Additionally, a low-dimensional chaotic map is designed to improve execution efficiency, and an adaptive threshold model is utilized to enhance compression performance.