Empirical Analysis of Robust Chaotic Maps for Image Encryption
Sonal Ayyappan1, C Lakshmi2
1Sonal Ayyappan*, Department of Computer Science and Engineering, SRMIST, Chennai, India.
2C Lakshmi, Department of Software Engineering, SRMIST, Chennai, India.
Manuscript received on August 19, 2020. | Revised Manuscript received on August 24, 2020. | Manuscript published on September 10, 2020. | PP: 393-398 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78500991120 | DOI: 10.35940/ijitee.K7850.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rate of transferring data in the form of text, image, video or audio over an open network has drastically increased. As these are carried out in highly sophisticated fields of medicine, military and banking, security becomes important. Inorder to enhance security for transmission, encryption algorithms play a vital role. So as to enhance the proficiency of the existing encryption methods and for stronger anti attack abilities, chaotic based cryptography is essential. Chaotic based encryption has advantages of being sensitive to initial conditions and control parameters. Images have features like bulk data capacity and high inter pixel correlation. Transmission of such medical data should be highly confidential, integral and authorized. Hence chaos-based image encryption is an efficient way of fast and high-quality image encryption. It has features like good speed, complexity, highly secure, reasonable computational overhead and power. In this paper a comprehensive analysis and an evaluation regarding the capabilities of different discrete time domain chaotic maps were carried out on a proposed image encryption method. The experimental results show high efficiency for the proposed image encryption technique.
Keywords: Chaotic maps, Cryptography, Image encryption, Security Analysis, Measurement Metrics.