Advertisement

Performance Study of Combined Artificial Neural Network Algorithms for Image Steganalysis

  • P. Sujatha
  • S. Purushothaman
  • R. Rajeswari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)

Abstract

Steganalaysis is a technique for detecting the presence of hidden information. Artificial neural network (ANN) is a widespread method for steganalysis. Back propagation algorithm (BPA), radial basis function (RBF), and functional update back propagation algorithm (FUBPA) are some of the popular ANN algorithms for detecting hidden information. Training and testing performance is improved when two algorithms are combined instead of using them separately. This paper analyzes the performance of combined algorithms of BPARBF and FUBPARBF. Among the two combinations FUBPARBF provides promising results than BPARBF since FUBPA uses less number of iterations for the network to converge. But still organizing the retrieved information is a challenging task.

Keywords

Back propagation algorithm Functional update back propagation algorithm Radial basis function Carrier image Information image 

References

  1. 1.
    Martin, A., Sapiro, G., Seroussi, G.: Is image steganography natural? IEEE Trans. Image Process. 14(12), 2040–2050 (2005)CrossRefGoogle Scholar
  2. 2.
    Sullivan, K., Bi, Z., Madhow, U., Chandrasekaran, S., Manjunath, B.S.: Steganalysis of quantization index modulation data hiding. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 1165–1168 Singapore (2004)Google Scholar
  3. 3.
    Fu, D., Shi Y.Q., Zou, D., Xuan, G.: JPEG steganalysis using empirical transition matrix in block DCT domain. In: Proceedings of 8th IEEE International workshop on Multimedia Signal Processing, pp. 310–313 Victoria, BC (2006) Google Scholar
  4. 4.
    Shi, Y.Q., Xuan, G., Zou, D., Gao, J., Yang, C., Zhang, Z., Chai, P., Chen, W., Chen, C.: Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. IEEE International Conference on Multimedia and Expo, ICME, Newark pp. 1-4, 6–8 July 2005 Google Scholar
  5. 5.
    Mehrabi, M.A., Faez, K., Bayesteh, A.R.: Image steganalysis based on statistical moments of wavelet sub band histograms in different frequencies and support vector machine. In: 3rd International Conference on Natural Computation,Haikou, vol. 1, pp. 587–590 August 2007Google Scholar
  6. 6.
    Wang, Y., Moulin, P.: Optimized feature extraction for learning-based image steganalysis. IEEE Trans. Inform. Forens. Secur. 2(1), 31–45 (2007)CrossRefGoogle Scholar
  7. 7.
    Gul, G., Kurugollu, F.: SVD based universal spatial domain image steganalysis. IEEE Trans. Inform. Forens. Secur. 5(2), 349–353 (2010)CrossRefGoogle Scholar
  8. 8.
    Avcibas, I., Memon, N., Sankur, B.: Steganalysis using image quality metrics. IEEE Trans. Image Process. 12(2), 221–229 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zhi, L., Fen, S.A. Detection of random LSB image steganography. In: 60th IEEE International Conference on Vehicular Technology, China vol. 3, pp. 2113-2117 September 2004Google Scholar
  10. 10.
    Chandramouli, R.: A mathematical framework for active steganalysis. ACM Multimedia Syst. 9(3), 303–311 (2003)CrossRefGoogle Scholar
  11. 11.
    Farid H.: Detecting hidden messages using higher-order statistical models. In: Proceedings of IEEE International Conference on Image Processing, pp. 905–908 New York (2002) Google Scholar
  12. 12.
    Wu, X., Dumitrescu, S., Wang, Z.: Detection of LSB steganography via sample pair analysis. In: 5th International Workshop on Information Hiding, London pp. 355–372 (2002) Google Scholar
  13. 13.
    Yu, X.Y., Wang, A.: Steganalysis based on regression model and Bayesion network. In: International Conference on Multimedia Information Networking and Security, MINES ‘09.Hubei, vol. 1, pp.41–44 (2009)Google Scholar
  14. 14.
    Lin, J.-Q.,Zhong, S.-P.: JPEG image steganalysis method based on binary similarity measures. International Conference on Machine Learning and Cybernetics. vol. 4, 2238–2243 (2009)Google Scholar
  15. 15.
    Zhao, X., Huang, L., Li, L., Yang, W., Chen, Z., Yu, Z.: Steganalysis on character substitution using support vector machine knowledge discovery and data mining, Second International Workshop, Moscow pp. 84–88 (2009)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science VELS UniversityChennaiIndia
  2. 2.PET Engineering CollegeTirunelveliIndia
  3. 3.Mother Teresa Women’s UniversityKodaikanalIndia

Personalised recommendations