IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography

A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography
View Sample PDF
Author(s): Ganavi M (JNNCE)and Prabhudeva S (JNNCE)
Copyright: 2021
Volume: 7
Issue: 1
Pages: 17
Source title: International Journal of Rough Sets and Data Analysis (IJRSDA)
Editor(s)-in-Chief: Parikshit Narendra Mahalle (Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust's, Vishwakarma Institute of Information Technology, India)
DOI: 10.4018/IJRSDA.288520

Purchase

View A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography on the publisher's website for pricing and purchasing information.

Abstract

Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by attackers. There is a necessity for a better encryption method to enhance information security. The distinctive features of Elliptic Curve Cryptography (ECC) in particular the key atomity, speedy ciphering and preserving bandwidth captivating its use in multimedia encipher. An encryption method is proposed by incorporating ECC, Secure Hash Algorithm – 256 (SHA-256), Arnold transform, and hyperchaos. Randomly generated salt values are concatenated with each pixel of an image. SHA-256 hash is imposed which produces a hash value of 32-bit, later used to generate the key in ECC. Stronger ciphering is done by applying Arnold’s transformation and hyperchaos thereby achieved more randomness in image. Simulation outcomes and analysis show that the proposed approach provides more confidentiality for color images.

Related Content

Tianlong Wang, Chaoyang Wang, Zhiqiang Liu, Shuai Ma, Huibo Yan. © 2024. 15 pages.
Xudong Cao, Chenchen Chen, Lejia Zhang, Li Pan. © 2024. 25 pages.
Shengfeng Xie, Jingwei Li. © 2024. 20 pages.
Xiaoyuan Wang, Hongfei Wang, Jianping Wang, Jiajia Wang. © 2024. 24 pages.
Jiao Hao, Zongbao Zhang, Yihan Ping. © 2024. 14 pages.
Qinmei Wang. © 2024. 13 pages.
Wenzhen Mai, Mohamud Saeed Ambashe, Chukwuka Christian Ohueri. © 2024. 18 pages.
Body Bottom