Utilizing Statistical Models for Proposing Hybrid Noise Approach of Image Encryption

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    A proposed strategy for an encryption of image based on image noise by statistical models is represented in this paper. An image encryption algorithm has been designed and simulated for some of the common various types of image noise. The proposed method (Hybrid Noise) consisting of a composition of more than one of probability density functions.

    Further, the hybrid approach has been compared with some standard noise types such as Gaussian, salt, and pepper, and speckle noise based on some of the performance scales like Peak Signal to Noise Ratio, variance and standard deviation and other comparison tools. The results show that our hybrid method has more security of image with less Peak Signal to Noise Ratio values, which means high noise level.

     

     


  • Keywords


    Statistical Model, Image Encryption, Image Noise, Impulse Noise, Salt and Pepper Noise, Gaussian Noise, Peak Signal to Noise Ratio (PSNR).

  • References


      [1] Albertus, J. S., Lukito, E. N., Gede, B. S., & Risanuri, H. (2011). Compression Ratio and Peak Signal to Noise Ratio in Grayscale Image Compression using Wavelet. International Journal of Computer Sci ence and Technology.

      [2] Gravel, P., Beaudoin,G., & De Guise, J. (2004). A Method for Modeling Noise in Medical Images. IEEE TRANSACTIONS ON MEDICAL IMAGING, 1221-1232.

      [3] McAndrew, A. (2004). An Introduction to Digital Image Processing with Matlab. Victoria University of Technology, USA.

      [4] Mohammed, A. K., Hashim, H., Hussein, A., & Mohammed, H. (2017). An Algorithm Based on GSVD for Image Encryption. mathematical and Computational Applications.

      [5] Pal, G., & Verma, V. K. (2017). Image Encryption using Adaptive Pixel Masking under Various Noise Attacks. International Journal of Computer Applications, 0975 – 8887.

      [6] Tuncer, A. C., & Kenneth, B. E. (2007). Rayleigh Maximum-Likelihood Filtering for Speckle Reduction of Ultrasound Images. IEEE TRANSACTIONS ON MEDICAL IMAGING, 26(5).

      [7] Wang, Z., & Bovik, A. (2006). Modern Image Quality Assessment. Morgan & Claypool Publishers: Synthesis Lectures on Image, Video, &Multimedia Processing.

      [8] Wang, Z., & Bovik, A. (2009). Mean squared error: Love it or leave it? a new look at signal fidelity measures. Signal Processing Magazine, IEEE, vol. 26, no. 1, pp. 98 –117, .


 

View

Download

Article ID: 16981
 
DOI: 10.14419/ijet.v7i3.19.16981




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.