Abstract
This research introduces a method of steganalysis by means of neural networks and its structure optimization. The main aim is to explain the approach of revealing a hidden content in jpeg files by feed forward neural network with Levenberg-Marquardt training algorithm. This work is also concerned to description of data mining techniques for structure optimization of used neural network. The results showed almost 100% success of detection.
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References
Cole, E., Krutz, D.R.: Hiding Sight, 321 p. Wiley Publishing, Inc., United States of America (2003) ISBN 0-471-44449-9
Goldwasser, S., Bellare, M.: Lecture Notes on Cryptography, 283 p. MIT, Cambridge (2001), http://cseweb.ucsd.edu/~mihir/papers/gb.pdf
Hertz, J., Kogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison–Wesley (1991)
Provos, N., Honeyman, P.: Detecting Steganographic Content on the Internet. CITI Technical Report 01-11 (2001)
Provos, N., Honeyman, P.: Hide and Seek: An Introduction to Steganography. IEEE Security & Privacy 1(3), 32–44 (2003)
Westfeld, A., Pfitzmann, A.: Attacks on Steganographic Systems. In: Proceedings of Information Hiding - Third International Workshop. Springer (September 1999)
Fridrich, J., Goljan, M., Hogea, D.: Steganalysis of JPEG images: Breaking the F5 algorithm. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 310–323. Springer, Heidelberg (2003), http://www.ws.binghamton.edu/fridrich/Research/f5.pdf (last accessed: December 24, 2003)
Fridrich, J., Goljan, M., Hogea, D.: New Methodology for Breaking Steganographic Techniques for JPEGs. Submitted to SPIE: Electronic Imaging 2003, Security and Watermarking of Multimedia Contents, Santa Clara, California (2003)
Fridrich, J.: Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 67–81. Springer, Heidelberg (2004)
Farid, H., Lyu, S.: Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 340–354. Springer, Heidelberg (2003)
Hempstalk, K.: Hiding Behind Corners: Using Edges in Images for Better Steganography. In: Proceedings of the Computing Women’s Congress, Hamilton, New Zealand, February 11-19 (2006)
Gül, G., Dirik, A.E., Avcibas, I.: Steganalytic Features for JPEG Compression-Based Perturbed Quantization. IEEE Signal Processing Letters 3 (March 2007)
Matousek, R.: Using AI Methods to Find a Non-Linear Regression Model with a Coupling Condition. Engineering Mechanics 17(5/6), 419–431 (2011) ISSN: 1802-1484
Volna, E.: Learning and evolution in artificial neural networks: Comparison study. In: ANNIP 2008, Madeira, Portugal, pp. 10–17 (2008) ISBN: 978-989-8111-33-3
Volna, E.: Design of neural network trees. In: Mendel 2008, pp. 978–980. Technical University of Brno (2008) ISBN: 978-80-214-3675-6
Zelinka, I.: SOMA – Self Organizing Migrating Algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, 33, ch. 7. Springer (2004)
Price, K.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill, London
Matousek, R.: HC12: The Principle of CUDA Implementation. In: MENDEL 2010. Mendel Journal series, pp. 303–308. VUT, Brno (2010) ISBN: 978-80-214-4120-0, ISSN: 1803-3814
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn., pp. 385–392. MIT Press and McGraw-Hill, Cambridge (2001) ISBN 0-262-03293-7. Section 16.3
ImageMagick, http://www.imagemagick.org/script/index.php
Obayashi, S., Jeong, S., Chiba, K.: Multiobjective Design Exploration for Aerodynamic Configurations. American Institute of Aeronautics and Astronautics (August 22, 2007)
Mosavi, A.: Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools. IJCSI International Journal of Computer Science Issues 7(2(1)) (March 2010) ISSN (Online): 1694-0784
Witten, I.H., Frank, E., Hall, M.A.: Datamining – Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2011) ISBN: 978-0-12-374856-0
Software OutGuess, http://www.outguess.org
Defending Against Statistical Steganalysis Niels Provos. In: 10th USENIX Security Symposium, Washington, DC (August 2001)
Hetzl S.: Steghide (1) - Linux man page, http://steghide.sourceforge.net/documentation/manpage.php (cit. May 21, 2008)
Westfeld, A.: High Capacity Despite Better Steganalysis (F5-—A Steganographic Algorithm). In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 289–302. Springer, Heidelberg (2001)
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Oplatkova, Z., Holoska, J., Prochazka, M., Senkerik, R., Jasek, R. (2013). Optimization of Artificial Neural Network Structure in the Case of Steganalysis. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_32
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DOI: https://doi.org/10.1007/978-3-642-30504-7_32
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