CFP last date
20 January 2025
Reseach Article

Reversion-based Features Video Steganalysis for Images and Image Fusion Technique using Fuzzy Logic

Published on December 2013 by Punithalatha. M, Amsaveni. A
International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
Foundation of Computer Science USA
ICIIIOES - Number 12
December 2013
Authors: Punithalatha. M, Amsaveni. A
61d91ca5-6d9e-433c-b856-4b26834f2663

Punithalatha. M, Amsaveni. A . Reversion-based Features Video Steganalysis for Images and Image Fusion Technique using Fuzzy Logic. International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences. ICIIIOES, 12 (December 2013), 15-22.

@article{
author = { Punithalatha. M, Amsaveni. A },
title = { Reversion-based Features Video Steganalysis for Images and Image Fusion Technique using Fuzzy Logic },
journal = { International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences },
issue_date = { December 2013 },
volume = { ICIIIOES },
number = { 12 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 15-22 },
numpages = 8,
url = { /proceedings/iciiioes/number12/14365-1448/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%A Punithalatha. M
%A Amsaveni. A
%T Reversion-based Features Video Steganalysis for Images and Image Fusion Technique using Fuzzy Logic
%J International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%@ 0975-8887
%V ICIIIOES
%N 12
%P 15-22
%D 2013
%I International Journal of Computer Applications
Abstract

Video Steganography is a method toward hide some kind of files within any expansion keeps on carrying Video file. Video steganography in spatial / transformation area, motion vector (MV)-based method objective is to target the interior dynamics of video compression and embed messages whereas performing arts motion estimation. Several methods adopt nonoptimal collection of rules and adapt the changes in MVs arbitrary manners which break the encoding principles a lot. It aims on the fault of the video steganography. To conquer these difficulties we intend a calibration-based approach and proposition of MV reversion- based features used for steganalysis. MV-based steganography share a number of features into common, i. e. , they primary choose a subset of MVs follow a predefined selection rule (SR). Motion-compensated prediction be an essential part of video compression and its necessary idea to predict the frame toward be coded using one or more earlier coded frames. We enhance our work by means of image fusion technique . Image Fusion produces a single image through by combining information beginning a set of source images together using pixel, feature. The fused image contains superior information content intended for the scene than any one of the character image sources only. In image fusion technique is a hypothetical framework for the aggregation procedure based on the make use of fuzzy logic approach . The fusion operators to improve the conventional fusion process with the introduction of spatial information modelling. Fuzzy logic modelling utilizes the fuse images from different scenes. In a novel hybrid multispectral image fusion based on fuzzy logic approach is proposed using combine framework of wavelet transform and it provides narrative resolution among the spectral and spatial fidelity. An image fusion algorithm based on fuzzy logic and wavelet proposed algorithm based on the discrete wavelet transform. It uses a nonlinear fuzzy fusion rule to combine features that are extracted from the original images.

References
  1. J. Fridrich, "Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes," in Proc. IH'04, Lecture Notes in Computer Science, 2004, vol. 3200/2005, pp. 67–81.
  2. Deepika R. Chaudhari, Ranjit Gawande", Data hiding in Motion Vectors of Compressed Video Based On their Associated Prediction Error", International Journal of Emerging Technology and Advanced Engineering Website: ISSN 2250-2459, Volume 2, Issue 10, and October 2012.
  3. Changyong Xu, Xijian Ping, Tao Zhang," Steganography in Compressed Video Stream", Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06), 2006.
  4. Farid H. and Siwei, L,"Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines "5th International Workshop. Lecture Notes in Computer Science, Vol. 2578. Springer-Verlag, Berlin Hei-delberg New York 2002.
  5. Tzschoppe. , Bäuml, R. , Huber, J. B. , and Kaup, A," Steganographic System based on Higher-Order Statistics", Proc. EI SPIE Electronic Imaging, pp 156–166, Santa Clara 2003.
  6. Ligia Chiorean & Mircea Florin Vaida, "Medical Image Fusion Based on Discrete Wavelet Transform Using Java Technology", Proceedings of the ITI, pp55-60, 2009.
  7. Prakash NK ,"Image Fusion Algorithm Based on Biorthogonal wavelet", International Journal of Enterprise Computing and Business Systems, Vol. 1, No. 2, pp1-6,2011.
  8. Krista Amolins & Yun Zhang, Peter Dare, "Wavelet based image fusion techniques – An introduction, review and comparison", ISPRS Journal of Photogrammetric & Remote Sensing, Vol. 62, No. 4, pp249–263, 2007.
  9. Tanish Zaveri, Mukesh Zaveri, Ishit Makwana, Mitul Panchal &Vitrag Sheth, "A Region based Pan Sharpening Method using Match Measure and Fuzzy Logic", Annual IEEE India Conference, pp257-260,2009.
  10. Julien Montagner & Vincent Barra, "Multilevel Information Fusion: A Mixed Fuzzy Logic/Geometrical Approach with Applications in Brain Image Processing", pp490-513, 2009.
  11. D. Fang and L. Chang, "Data hiding for digital video with phase of motion vector," in Proc. Int. Symposium on Circuit and Systems (ISCAS)[C], 2006, pp. 1422–1425.
  12. M. J. Gormish and J. T. Gill, "Computation-rate-distortion in transform coders for image compression," SPIE Vis. Commun. Image Process. pp. 146–152, 1993.
  13. Laure J Chip man & Timothy M Orr, (1995) "Wavelets and Image Fusion", Proceedings of SPIE, Vol. 2569, No. 208, pp248-251.
  14. Ligia Chiorean & Mircea Florin Vaida, (2009) "Medical Image Fusion Based on Discrete Wavelet Transform Using Java Technology", Proceedings of the ITI, pp55-60.
  15. Prakash NK (2011)"Image Fusion Algorithm Based on Biorthogonal wavelet", International Journal of Enterprise Computing and Business Systems, Vol. 1, No. 2, pp1-6.
  16. Krista Amolins & Yun Zhang, Peter Dare, (2007) "Wavelet based image fusion techniques – An introduction, review and comparision", ISPRS Journal of Photogrammetric & Remote Sensing, Vol. 62, No. 4, pp249–263.
  17. Susmitha Vekkot, & Pancham Shukla, (2009) "A Novel Architecture for Wavelet based Image Fusion", World Academy of Science, Engineering and Technology, Vol. 57, pp372-377.
  18. Yi Zheng & Ping Zheng, (2010) "Multisensor Image Fusion using Fuzzy Logic for Surveillance Systems", Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 2, pp588-592.
  19. The Online Resource for Research in Image Fusion, http://www. imagefusion. org.
  20. Maruthi R & Sankarasubramanian K, (2008) "Pixel Level Multifocus Image Fusion Based on Fuzzy Logic Approach", Asian Journal of Information Technology, Vol 7, No. 4,pp168-171.
  21. Thomas J. Meltzer, Davis Bednorz, Sohn E. J, Kimberly Lane & Darryl Bryk, (2002) "Fuzzy Logic Based Image Fusion", pp1-9.
Index Terms

Computer Science
Information Sciences

Keywords

Image Fusion Wavelet Transform Genetic Algorithm Fuzzy Logic.