Video Encryption Using Chaos-Based Information Entropy

9 Pages Posted: 30 Nov 2020

See all articles by Geetha N

Geetha N

Alagappa University

Dr.Mahesh K

Alagappa University

Date Written: November 21, 2020

Abstract

A chaotic video encryption algorithm based on information entropy (IEAIE) is proposed. This undertaking examines the security properties of the calculation and assesses the legitimacy of the utilized quantifiable security measurements. While any calculation distributed goes through some pretty much severe test security investigation, huge numbers of those plans are being broken in resulting distributions. In this undertaking, show that two primary inspirations for leaning toward turmoil based video encryption over old-style solid cryptographic encryption, to be specific computational exertion and security benefits, are profoundly sketchy. Show that few factual tests, regularly used to evaluate the security of bedlam based encryption plans, are lacking measurements for security examination. At the point when the round number is just one, the proportionate mystery key of each essential activity of IEAIE can be recouped with a differential assault independently. Some normal weakness issues in the field of disordered picture encryption are found in IEAIE, for example, the short circles of the computerized confused framework and the invalid affectability component based on data entropy of the plain picture. Far and away more terrible, every security metric is flawed, which subverts the security validity of IEAIE. Consequently, IEAIE can just fill in as a counterexample for representing basic entanglements in planning secure specialized technique for picture information.

Keywords: Image Encryption, Quantitative Security, Analytic Thinking, Statistical Testing, Chaotic Video Encryption

Suggested Citation

N, Geetha and K, Dr.Mahesh, Video Encryption Using Chaos-Based Information Entropy (November 21, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3734878 or http://dx.doi.org/10.2139/ssrn.3734878

Geetha N (Contact Author)

Alagappa University ( email )

Karaikudi, Tamil Nadu
India

Dr.Mahesh K

Alagappa University ( email )

Karaikudi, Tamil Nadu
India

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