article · AIP Advances
A new framework integrates multiple chaotic maps, including logistic, tent, quadratic, cubic, and Bernoulli maps, to enhance the security and privacy of image encryption. The method creates more robust chaotic maps by using variable keys produced from a sine square logistic map. Because different maps affect images differently, the optimal chaotic map for encoding each image is selected based on achieving the lowest correlation factor, as smaller correlation values indicate superior encryption. Numerical experiments conducted on various grey images demonstrated that the system effectively encrypts and decrypts visual data. Performance analyses revealed that this integrated pipeline outperforms established techniques, including circular mapping, standard substitution boxes, and substitution boxes paired with the Arnold transform, delivering an excellent security level marked by low correlation coefficients and strong information entropy.
Securing digital images during transmission and storage is crucial for privacy and data protection. By combining several chaotic mathematical models and dynamically choosing the most effective map for a given image, this approach strengthens cryptographic defences. It helps prevent unauthorised reconstruction of sensitive visual information while outperforming several conventional encryption techniques in security metrics.
The framework is designed for secure image encryption and decryption, which is relevant to developers of visual data protection software and secure communications systems. Tested numerically on grey images in a research setting, the technology represents early-stage, algorithm-level research. Substantial further software engineering, testing on colour images, and computational efficiency assessments would be required before real-world commercial deployment.
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In this paper, a novel framework is presented for chaotic image encryption. The proposed method is based on integrating multiple chaotic maps (e.g., logistic, tent, quadratic, cubic, and Bernoulli) to generate more robust chaotic maps in order to increase the security and privacy needed by applying variable keys. The latter are generated by computing the sine square logistic map and are then applied to generate the chaotic maps employed in our framework. For this, we have performed many experiments to achieve the best period for each chaotic map in which it performed the best encryption. Here, we combine multiple chaotic maps to get a new map that works well when X ∈ [0, 1]. For using a chaotic map in the encryption process, it was necessary to find a way to choose the best of those chaotic maps for encryption. This selection was done with the lowest value for the correlation factor because the smaller value of correlation has an impression of good encryption. We have also noted a clear difference in the influence of one of these maps on some pictures from the others. We chose one of those maps according to the correlation value for each encoding process and compared them. Then, we used a chaotic map of the best of these values for encryption and decryption. Numerical results on various gray images showed the robustness of the proposed method to encrypt and decrypt the images based on the evaluation using different performance analyses. We compared our methods against other well-known approaches, e.g., circular mapping, S-boxes, and S-box with Arnold transform. Our pipeline outperforms those methods. Moreover, our results documented that the proposed scheme has an excellent security level with very low correlation coefficients and good information entropy.
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DOI: 10.1063/5.0009225
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