A new hierarchical josephs' chaos image encryption method combined with parallel compressive sensing

CN122179519BActive Publication Date: 2026-07-24HUAQIAO UNIVERSITY

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Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: HUAQIAO UNIVERSITY
Filing Date: 2026-05-13
Publication Date: 2026-07-24

AI Technical Summary

Technical Problem

Existing compressed sensing image encryption methods struggle to balance security and reconstruction quality. The randomness of the measurement matrix and the cross-correlation of sparse bases lead to a decline in reconstruction quality, and they lack robustness against cropping and noise in unreliable network environments.

Method used

A hierarchical Josephus scrambling image encryption method combining parallel compressed sensing is adopted. The measurement and encryption matrices are generated using chaotic sequences. Through an adaptive sparse strategy and hierarchical Josephus scrambling, high randomness and high quality reconstruction of images are achieved, enhancing noise robustness.

Benefits of technology

While ensuring encryption security, it improves image reconstruction quality and has strong anti-cropping and anti-noise robustness in unreliable network environments, reducing consumption during transmission.

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Abstract

The application provides a new layered Josephs scrambling image encryption method combining parallel compressive sensing, and relates to the technical field of image communication, and the method comprises the following steps: S10, an original image is acquired and an initial key is generated, and the initial key is input into a chaotic system to iteratively generate a chaotic sequence group; S20, a measurement matrix required by parallel compressive sensing and an encryption matrix required by image encryption are transformed based on the chaotic sequence group; S30, an adaptive sparse strategy is established based on the correlation between an oversampling factor and a preset compression ratio, and the original image is transformed into a target sparse matrix through the adaptive sparse strategy; S40, a compressive sensing operation is performed on the target sparse matrix based on the measurement matrix, and a compressed image matrix is obtained; and S50, layered Josephs scrambling and diffusion operations are performed on the compressed image matrix based on the encryption matrix, and a target ciphertext image is obtained, so that the image reconstruction quality and encryption randomness can be considered.

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