Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : R Sivamalar | Dr Swati Sharma

Volume 04 Issue 06 2017

ISSN no:  2348-3121

Page no: 60-64

Abstract – Due to the rapid growth of optical encryption and compression techniques, authentication of encoded information has the most significant process for providing high levels of optical security. The present optical encryption and compression methods can provide the secure access to the information and such techniques are improving day by day. In previous researches, hybrid optical encryption and compression was proposed with multiplexing scheme for encrypting and compressing the multiple images simultaneously. However, the classification of the encoded images using SVM and K-NN was simple and easily to distinguish for identifying the images which are obtained from fake samples and genuine samples. Hence in this paper, an Extreme Learning Machine (ELM) based classification is proposed for classifying the images which are encoded using the different approaches in order to evaluate the encoded performance. The main aim of this paper is to develop an optical system which has the highest-level of optical security with high identification complexity for identifying the encoded images. Finally, the experimental results show that improved security level by reducing the classification accuracy of encoded image identification.

Keywords – Optical encryption and compression; Multiplexing; Encoding; Optical security; Classification; Extreme learning machine


  1. W. Chen, B. Javidi, and X. Chen, “Advances in optical security systems”, Advances in Optics and Photonics, vol. 6, no. 2, pp. 120-155, 2014.
  2. R. Sivamalar, and S. Sharma, “An optical image encryption using chaotic kicked rotator map with double random phase encoding”, International Journal of Applied Research in Science and Engineering, pp. 118-123, 2016.
  3. R. Sivamalar, and S. Sharma, “Simultaneous encryption and compression using chaotic kicked rotator map–DRPE with direction adaptive discrete wavelet transform”, International Journal for Technological Research in Engineering, pp. 170-174, 2016.
  4. H. T. Chang, J. W. Shui, and K. P. Lin, “Image multiplexing and encryption using the non negative matrix factorization method adopting digital holography”, Applied Optics, vol. 56, no. 4, pp. 958-966, 2017.
  5. L. Gong, and A. K. Nandi, “An enhanced initialization method for non-negative matrix factorization”, IEEE International Workshop on Machine Learning for Signal Processing, pp. 1-6, 2013.
  6. S. Liu, and J. T. Sheridan, “Optical encryption by combining image scrambling techniques in fractional Fourier domains”, Optics Communications, vol. 287, pp. 73-80, 2013.
  7. Q. Gong, X. Liu, G. Li, and Y. Qin, “Multiple-image encryption and authentication with sparse representation by space multiplexing”, Applied optics, vol. 52, no. 31, pp. 7486-7493, 2013.
  8. S. K. Rajput, D. Kumar, and N. K. Nishchal, “Photon counting imaging and polarized light encoding for secure image verification and hologram watermarking”, Journal of Optics, vol. 16, no. 12, 125406, 2014.
  9. J. Lai, S. Liang, and D. Cui, “A novel image encryption algorithm based on fractional Fourier transform and chaotic system”, International Conference on Multimedia Communications, pp. 24-27, 2010.
  10. H. T. Chang, H. E. Hwang, and C. L. Lee, “Position multiplexing multiple-image encryption using cascaded phase-only masks in Fresnel transform domain”, Optics Communications, vol. 284, no. 18, pp. 4146-4151, 2011.
  11. H. T. Chang, H. E. Hwang, C. L. Lee, and M. T. Lee, “Wavelength multiplexing multiple-image encryption using cascaded phase-only masks in the Fresnel transform domain”, Applied optics, vol. 50, no. 5, pp. 710-716, 2011.
  12. N. K. Nishchal, and T. J. Naughton, “Flexible optical encryption with multiple users and multiple security levels”, Optics Communications, vol. 284, no. 3, pp. 735-739, 2011.
  13. E. Pérez-Cabré, M. Cho, and B. Javidi, “Information authentication using photon-counting double-random-phase encrypted images”, Optics letters, vol. 36, no. 1, pp. 22-24, 2011.
  14. A. Carnicer, and B. Javidi, “Optical security and authentication using nano scale and thin-film structures”, Advances in Optics and Photonics, vol. 9, no. 2, pp. 218-256, 2017.
  15. H. Zhang, S. Zhang, and Y. Yin, “An improved ELM algorithm based on EM-ELM and ridge regression”, International Conference on Intelligent Science and Big Data Engineering, pp. 756-763, 2013.