Image cryptographic algorithm based on the Haar wavelet transform

Tedmori, Sara; Al-Najdawi, Nijad

doi:10.1016/j.ins.2014.02.004

Cited by 92 publications

(32 citation statements)

References 26 publications

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“…For an optimal estimation, S k represents the intensity of the image, k represents the decomposed layer, and A k represents the range of the image in layer k. The combination of the FMW and the NE-LML filter allows the detection of appropriate areas to embed information in order to obtain the stego-image. [29].…”

Section: Estimation Of Noise Standardmentioning

confidence: 99%

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Carvajal-Gámez

Díaz-Casco

2018

Security and Communication Networks

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In recent years, thanks to the use of Internet services, daily activities used to imply movement became more accessible to any user. As a result of such interconnection, now millions of people from different countries are able to communicate among themselves through the Internet, generating a great flow of data and classified information. The information on the Internet can be stolen, intercepted, anonymized, or even destroyed, resulting in cases of infringement of intellectual property rights, and the loss or damage of data. In such a globalized and interconnected world, solid security measures have become increasingly important to ensure data privacy protection and its confidentiality during transit. Nowadays, there is a variety of security mechanisms such as the steganography, an information hiding technique, which protects intellectual property by allowing the transmission of hidden data without drawing any suspicion. In order to achieve these criteria, an adaptation of the nonlocal maximum likelihood filter is proposed; in this class of filters, in general, they are used in images that require a high level of irregular pattern detection, based on the statistical dependence of the underlying pixels of the image analysis area, when using it in the wavelet domain as edge detector and/or discontinuities in images in order to have a greater selectivity when inserting information in the image. It strengthens the detection of the areas with the highest probability of having noise presenting results which are suitable areas to insert the information and that it is imperceptible in a quantitative and qualitative manner as presented in the Results and Discussion.

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Section: Estimation Of Noise Standardmentioning

confidence: 99%

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Carvajal-Gámez

Díaz-Casco

2018

Security and Communication Networks

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“…Hence, the Haar wavelet transform is used less in cryptography. Researchers usually use of the Haar wavelet in cryptography based on the hybrid methods [25,26,27]. Here, we propose a new method which can improve this problem.…”

Section:  New Perspective Of Haar Wavelet Transformmentioning

confidence: 99%

Chaotic trigonometric Haar wavelet with focus on image encryption

Ahadpour

Sadra

2017

Journal of Discrete Mathematical Sciences and Cryptography

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In this paper, after reviewing the main points of Haar wavelet transform and chaotic trigonometric maps, we introduce a new perspective of Haar wavelet transform. The essential idea of the paper is given linearity properties of the scaling function of the Haar wavelet. With regard to applications of Haar wavelet transform in image processing, we introduce chaotic trigonometric Haar wavelet transform to encrypt the plain images. In addition, the encrypted images based on a proposed algorithm were made. To evaluate the security of the encrypted images, the key space analysis, the correlation coefficient analysis and differential attack were performed. Here, the chaotic trigonometric Haar wavelet transform tries to improve the problem of failure of encryption such as small key space and level of security.

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“…Multiscale processing algorithm is an effective tool to represent the phenomena occurred at different scales, see [23,25,5,11,12,17,42,18,28,19,6,7,32,43,29,30,26]. The http://dx.doi.org/10.1016/j.ins.2014.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao, Y. Jia / Information Sciences 296 (2015)[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] …”

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confidence: 99%

Transcale control for a class of discrete stochastic systems based on wavelet packet decomposition

Zhao

Jia

2015

Information Sciences

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Image cryptographic algorithm based on the Haar wavelet transform

Cited by 92 publications

References 26 publications

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Chaotic trigonometric Haar wavelet with focus on image encryption

Transcale control for a class of discrete stochastic systems based on wavelet packet decomposition

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