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Performance evaluation of different universal steganalysis techniques in JPG files

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Języki publikacji
EN
Abstrakty
EN
Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are “Wavelet based steganalysis”, “Feature Based Steganalysis”, “Moments of characteristic function using wavelet decomposition based steganalysis”, “Empirical Transition Matrix in DCT Domain based steganalysis”, and “Statistical Moment using jpeg2D array and 2D characteristic function”. A large Dataset of Images -1000 images- are subjected to three types of steganographic techniques which are “Outguess”, “F5” and “Model Based” with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The “Empirical Transition Matrix in DCT Domain based steganalysis” achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique.
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Strony
121--139
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • Computer Science Institute of Graduate Studies and Research Alexandria University
autor
  • Programming Language Information Technology Institute
  • Ministry of Communication and Information Technology
Bibliografia
  • [1] Kharrazi M., Sencar H. T., Memon N. , Benchmarking steganographic and Steganalysis techniques, Security, steganography, and watermarking of multimedia contents VII, San Jose CA (2005).
  • [2] Morkel T., Eloff J.H.P., Olivier M.S., An Overview of Image Steganography, Proceedings of the Fifth Annual Information Security South Africa Conference, Sandton, South Africa (2005).
  • [3] Anderson R. J., Petitcolas F. A. P., On The Limits of Steganography, IEEE Journal of Selected Areas in Communications, Special Issue on Copyright & Privacy Protection 474-48116 (1998).
  • [4] Silman J., Steganography and Steganalysis: An Overview, as part of the Information Security Reading Room, Copyright © SANS Institute (2001).
  • [5] Lee Y.K., Chen L.H., High capacity image steganographic model, Vision, Image and Signal Processing, IEE Proceedings 147 (2000): 288.
  • [6] Johnson N. F., Jajodia S., Steganalysis of Images Created Using Current Steganography Software, Lecture Notes in Computer Science 1525 (1998): 273.
  • [7] Wang H., Wang S., CyberWarfare: Steganography vs. Steganalysis, Communications of the ACM 47 (10) (2004): 76.
  • [8] Johnson N. F., Jajodia S., Exploring Steganography: Seeing the Unseen, IEEE Computer Journal 31 (2) (1998): 24.
  • [9] Provos N., Defending Against Statistical Steganalysis, a research supported by DARPA grant number F30602- 99- 1- 0527.
  • [10] Westfeld A., F5 a Steganographic Algorithm: High Capacity despite better Steganalysis, 4th International Workshop on Information Hiding. (2001).
  • [11] Sallee P., Model – based Steganography, International Workshop on Digital Watermarking, Seoul, Korea (2003).
  • [12] Chandramouli R., Subbalakshmi K. P., Current Trends in Steganalysis: A Survey, Department of ECE Stevens Institute of Technology.
  • [13] Farid H., Detecting Hidden Messages Using Higher – Order Statistical Models.
  • [14] Adelson E. H., Simoncelli E. P., Subband image coding with three-tap pyramids, In Picture Coding Symposium, publisher MIT Media Laboratory (1990).
  • [15] Vaidyanathan P., Quadrature mirror filter banks, M-band extensions and perfect-reconstruction techniques, ASSP Magazine IEEE 4 (3) (1987): 4.
  • [16] Vetterli M., A theory of multi rate filter banks, Acoustics, Speech and Signal Processing, IEEE Transactions 35 (3) (1987): 356.
  • [17] Fridrich J., Feature-Based Steganalysis for JPEG Images and its Implications for Future Design of Steganographic Schemes.
  • [18] Shi Y.Q., Xuan G., Zou D., Gao J., Ch. Yang Ch., 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 6 (8) (2005).
  • [19] Fu D., Shi Y. Q., Zou D., Xuan G., JPEG Steganalysis Using Empirical Transition Matrix in Block DCT Domain, Multimedia Signal Processing, 2006 IEEE 8th Workshop (2006).
  • [20] Chen C., Shi Y. Q., Chen W., Xuan G., Statistical Moments Based Universal Steganalysis Using JPEG 2-D array and 2-D Characteristic Function (2005).
  • [21] Support vector machine; http://en.wikipedia.org/wiki/Support_vector_machine; last visited 12/2007.
  • [22] Hsu C. W., Chang C. C., Lin C. J., A practical guide to support vector classification, Technical report, Department of Computer Science, National Taiwan University (2003).
  • [23] LIBSVM – A Library for Support Vector Machines; http://www.csie.ntu.edu.tw/∼cjlin/libsvm/, last visited 2/2008.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-6ccaf671-c184-47a6-bc41-322ed1f5ade4
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