Optimization of artificial neural network structure in the case of steganalysis

Komínková Oplatková, Zuzana; Hološka, Jiří; Procházka, Michal; Šenkeřík, Roman; Jašek, Roman

dc.title	Optimization of artificial neural network structure in the case of steganalysis	en
dc.contributor.author	Komínková Oplatková, Zuzana
dc.contributor.author	Hološka, Jiří
dc.contributor.author	Procházka, Michal
dc.contributor.author	Šenkeřík, Roman
dc.contributor.author	Jašek, Roman
dc.relation.ispartof	Intelligent Systems Reference Library
dc.identifier.issn	1868-4394 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-3-642-30503-0
dc.date.issued	2013
utb.relation.volume	38
dc.citation.spage	821
dc.citation.epage	843
dc.type	article
dc.language.iso	en
dc.publisher	Springer Science+Business Media	en
dc.identifier.doi	10.1007/978-3-642-30504-7_32
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-642-30504-7_32
dc.description.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. © Springer-Verlag Berlin Heidelberg 2013.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1003510
utb.identifier.obdid	43870005
utb.identifier.scopus	2-s2.0-84885457085
utb.source	j-scopus
dc.date.accessioned	2013-11-11T08:24:18Z
dc.date.available	2013-11-11T08:24:18Z
utb.contributor.internalauthor	Komínková Oplatková, Zuzana
utb.contributor.internalauthor	Hološka, Jiří
utb.contributor.internalauthor	Procházka, Michal
utb.contributor.internalauthor	Šenkeřík, Roman
utb.contributor.internalauthor	Jašek, Roman
utb.fulltext.affiliation	Zuzana Oplatkova, Jiri Holoska, Michal Prochazka, Roman Senkerik, and Roman Jasek Zuzana Oplatkova · Jiri Holoska · Michal Prochazka · Roman Senkerik · Roman Jasek Tomas Bata University in Zlín, Faculty of Applied Informatics, Nam. T.G. Masaryka 5555, 760 01 Zlín, Czech Republic e-mail: {oplatkova,holoska,mprochazka,senkerik,jasek}@fai.utb.cz
utb.fulltext.dates	-
utb.fulltext.references	[1] Cole, E., Krutz, D.R.: Hiding Sight, 321 p. Wiley Publishing, Inc., United States of America (2003) ISBN 0-471-44449-9 [2] Goldwasser, S., Bellare, M.: Lecture Notes on Cryptography, 283 p. MIT, Cambridge (2001), http://cseweb.ucsd.edu/~mihir/papers/gb.pdf [3] Hertz, J., Kogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison–Wesley (1991) [4] http://steghide.sourceforge.net/ [5] http://linux01.gwdg.de/~alatham/stego.html [6] http://www.outguess.org/detection.php [7] http://www.outguess.org/ [8] Provos, N., Honeyman, P.: Detecting Steganographic Content on the Internet. CITI Technical Report 01-11 (2001) [9] Provos, N., Honeyman, P.: Hide and Seek: An Introduction to Steganography. IEEE Security & Privacy 1(3), 32–44 (2003) [10] Westfeld, A., Pfitzmann, A.: Attacks on Steganographic Systems. In: Proceedings of Information Hiding - Third International Workshop. Springer (September 1999) [11] 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) [12] 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) [13] 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) [14] 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) [15] 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) [16] http://www.cs.waikato.ac.nz/ml/weka/ [17] Gül, G., Dirik, A.E., Avcibas, I.: Steganalytic Features for JPEG Compression-Based Perturbed Quantization. IEEE Signal Processing Letters 3 (March 2007) [18] 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 [19] 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 [20] Volna, E.: Design of neural network trees. In: Mendel 2008, pp. 978–980. Technical University of Brno (2008) ISBN: 978-80-214-3675-6 [21] Zelinka, I.: SOMA – Self Organizing Migrating Algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, 33, ch. 7. Springer (2004) [22] 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 [23] 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 [24] 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 [25] ImageMagick, http://www.imagemagick.org/script/index.php [26] Obayashi, S., Jeong, S., Chiba, K.: Multiobjective Design Exploration for Aerodynamic Configurations. American Institute of Aeronautics and Astronautics (August 22, 2007) [27] 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 [28] Witten, I.H., Frank, E., Hall, M.A.: Datamining – Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2011) ISBN: 978-0-12-374856-0 [29] Software OutGuess, http://www.outguess.org [30] Defending Against Statistical Steganalysis Niels Provos. In: 10th USENIX Security Symposium, Washington, DC (August 2001) [31] Hetzl S.: Steghide (1) - Linux man page, http://steghide.sourceforge.net/documentation/manpage.php (cit. May 21, 2008) [32] 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)
utb.fulltext.sponsorship	This work was supported by the grants: internal grant agency of Tomas Bata University in Zlin IGA/44/FAI/10/D, grant NO. MSM 7088352101 of the Ministry of Education of the Czech Republic, grant of Grant Agency of Czech Republic GACR 102/09/1680 and by the European Regional Development Fund under the Project CEBIATech No. CZ.1.05/2.1.00/03.0089.
utb.fulltext.projects	IGA/44/FAI/10/D
utb.fulltext.projects	MSM 7088352101
utb.fulltext.projects	GACR 102/09/1680
utb.fulltext.projects	CZ.1.05/2.1.00/03.0089