Hyperchaotic Systems and DNA Coding-Based Color Image Encryption Algorithm with Compressive Sensing.
Saved in:
| Title: | Hyperchaotic Systems and DNA Coding-Based Color Image Encryption Algorithm with Compressive Sensing. |
|---|---|
| Authors: | Wang, Jiaxin1 Wang928.jx@qq.com, Zhang, Zhao2 zhangzhao333@hotmail.com, Zhou, Hongyan3 zhou321yan@163.com, Chen, Xue-Bo4 xuebochen@126.com |
| Source: | Engineering Letters. Jun2026, Vol. 34 Issue 6, p2154-2167. 13p. |
| Subjects: | Image encryption, Compressed sensing, Image compression, Chaos theory, Nucleic acids, Logistic maps (Mathematics) |
| Abstract: | To enhance the security, compression efficiency, and anti-attack capability of color images during the encryption transmission process, this paper proposes a color image encryption algorithm that integrates chaotic systems, DNA coding, and compressive sensing. This method first decomposes the original color image into its three component channels and then performs block processing. Pixel scrambling is achieved through the Logistic mapping to break the spatial correlation of the image. Subsequently, a high-complexity chaotic sequence is generated using the Chen hyperchaotic system, which dynamically drives the DNA coding rules and operation methods to perform diffusion operations on each image sub-block, thereby enhancing key sensitivity and anti-attack capability. On this basis, the 2D compressive sensing method is introduced to compress and re-encrypt the image, effectively reducing the redundancy of image data and transmission costs. Experimental results demonstrate that the algorithm performs well in key indicators, including information entropy, pixel change rate (NPCR), and average change intensity (UACI), while maintaining good decryption quality and anti-interference ability, even at a high compression ratio. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
