Literature DB >> 33805786

A New Algorithm for Digital Image Encryption Based on Chaos Theory.

Yaghoub Pourasad1, Ramin Ranjbarzadeh2, Abbas Mardani3.   

Abstract

In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme's effectiveness and show that this technique is a suitable choice for actual image encryption.

Entities:  

Keywords:  chaos random sequence; digital image encryption; discrete wavelet transform; image processing

Year:  2021        PMID: 33805786      PMCID: PMC7998182          DOI: 10.3390/e23030341

Source DB:  PubMed          Journal:  Entropy (Basel)        ISSN: 1099-4300            Impact factor:   2.524


  3 in total

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Journal:  Opt Express       Date:  2012-01-30       Impact factor: 3.894

2.  An efficient medical image encryption using hybrid DNA computing and chaos in transform domain.

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3.  Image Encryption Algorithm Based on Hyperchaotic Maps and Nucleotide Sequences Database.

Authors:  Ying Niu; Xuncai Zhang; Feng Han
Journal:  Comput Intell Neurosci       Date:  2017-03-14
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