Version 1
: Received: 15 December 2021 / Approved: 16 December 2021 / Online: 16 December 2021 (11:52:50 CET)
How to cite:
Chen, C.; Zhang, H.; Wu, B. Image Encryption Based on Arnod Transform and Fractional Chaotic. Preprints2021, 2021120269. https://doi.org/10.20944/preprints202112.0269.v1
Chen, C.; Zhang, H.; Wu, B. Image Encryption Based on Arnod Transform and Fractional Chaotic. Preprints 2021, 2021120269. https://doi.org/10.20944/preprints202112.0269.v1
Chen, C.; Zhang, H.; Wu, B. Image Encryption Based on Arnod Transform and Fractional Chaotic. Preprints2021, 2021120269. https://doi.org/10.20944/preprints202112.0269.v1
APA Style
Chen, C., Zhang, H., & Wu, B. (2021). Image Encryption Based on Arnod Transform and Fractional Chaotic. Preprints. https://doi.org/10.20944/preprints202112.0269.v1
Chicago/Turabian Style
Chen, C., Hongying Zhang and Bin Wu. 2021 "Image Encryption Based on Arnod Transform and Fractional Chaotic" Preprints. https://doi.org/10.20944/preprints202112.0269.v1
Abstract
In view of the problem of cracking easily and partial distortion of images after encryption or decryption, a novel image encryption and decryption algorithm based on Arnod Transform and fractional chaotic is proposed. To begin with, the Arnold transform is used to encrypt. So that the spatial confidence of the original image has been comprehensively disturbed. Secondly, the XOR involving the fractional order chaotic sequence is used to encrypt. The key sequence is dynamically generated to ensure the randomness and difference of key generation. When decryption is required, the first decryption is performed using the key and XOR. Then the second decryption is carried out by using the inverse Arnold transform, and finally the decrypted image is obtained. Experimental results show that the improved algorithm has achieved better performance in encryption and decryption.
Keywords
fractional derivative; Arnold Transform; XOR involving fractional order chaotic sequence; encryption and decryption
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.