Issue |
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
|
|
---|---|---|
Article Number | 06002 | |
Number of page(s) | 8 | |
Section | Session VI: Theoretical Computer Science | |
DOI | https://doi.org/10.1051/itmconf/20171106002 | |
Published online | 23 May 2017 |
A Self-adaptive Bit-level Color Image Encryption Algorithm Based on Generalized Arnold Map
1 Department of Mathematics, Shantou University, Shantou, Guangdong, 515063, China
2 School of Mathematics, Jiaying University, Meizhou, Guangdong, 514015, China
a Corresponding author: rsye@stu.edu.cn
A self-adaptive bit-level color image encryption algorithm based on generalized Arnold map is proposed. The red, green, blue components of the plain-image with height H and width W are decomposed into 8-bit planes and one three-dimensional bit matrix with size ze H×W×24 is obtained. The generalized Arnold map is used to generate pseudo-random sequences to scramble the resulted three-dimensional bit matrix by sort-based approach. The scrambled 3D bit matrix is then rearranged to be one scrambled color image. Chaotic sequences produced by another generalized Arnold map are used to diffuse the resulted red, green, blue components in a cross way to get more encryption effects. Self-adaptive strategy is adopted in both the scrambling stage and diffusion stage, meaning that the key streams are all related to the content of the plain-image and therefore the encryption algorithm show strong robustness against known/chosen plaintext attacks. Some other performances are carried out, including key space, key sensitivity, histogram, correlation coefficients between adjacent pixels, information entropy and difference attack analysis, etc. All the experimental results show that the proposed image encryption algorithm is secure and effective for practical application.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.