Paper Info
Title
Beta Chaotic Map Based Image Encryption Using Genetic Algorithm
Abstract
In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique.
Year
DOI
Venue
2018
10.1142/S0218127418501328
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
Keywords
Field
DocType
Beta chaotic map, nonsubsampled contourlet transform, genetic algorithm
Pattern recognition,Chaotic map,Control theory,Encryption,Artificial intelligence,Contourlet,Genetic algorithm,Mathematics
Journal
Volume
Issue
ISSN
28
11
0218-1274
Citations 
PageRank 
References 
0
0.34
30
Authors
2
Name
Order
Citations
PageRank
Manjit Kaur1238.41
Vijay Kumar222921.59