Analysis of alteration the chi-squared divergence for pixels brightness distributions by cover and stego images filtering

Authors

  • D. O. Progonov National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev, Ukraine

DOI:

https://doi.org/10.20535/RADAP.2018.75.54-60

Keywords:

digital image, steganalysis, chi-squared divergence

Abstract

Information protection of government agencies, organizations as well as private corporations is topical task today. Great attention is given to prevention of confidential information leakage by data transmission in global and local communication systems. Revealing and destruction of covert channels require investigation of information flows in communication systems with usage of steganalysis methods. Providing a high detection accuracy (more than 95 %) of formed stego files requires a priory information about features of steganographic methods, used for message embedding into cover files, such as digital images. It leads to significantly decrease the performance of widespread steganalysis in case of stego image formation according to unknown embedding methods. Therefore, it is required a development of universal (blind) stegdetectors, that allow reliable revealing stego images even in case of limited a priory information about used steganographic algorithm. One of the toughest challenges for known universal stegdetectors is revealing of stego images, formed according to advanced adaptive embedding methods. Feature of these methods is minimization of cover image parameters distortions by message hiding. The work is devoted to investigation the effectiveness of preliminary processing (filtering) of cover as well as stego images for improving the accuracy of blind stegdetectors. The case of usage the median and wiener filters for cover/stego image processing is analyzed. Based on the results of research it is revealed that preliminary processing of analyzed images with median and wiener filters gives opportunity to detect weak alterations of cover image’s pixels brightness distributions, caused by stego data embedding according to HUGO and WOW adaptive methods. It is shown that analysis of χ2-divergences between distributions for initial and processed cover as well as stego images allows increasing detection accuracy of universal stegdetectors. Obtained results allow improve stegdetector’s performance even in case of low payload a cover image (less than 10 %), when standard detection methods are inefficient.

Author Biography

D. O. Progonov, National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev

Progonov D. O., Postgraduate student

References

Fridrich J. (2009) Steganography in Digital Media. DOI: 10.1017/cbo9781139192903

Kodovský J. and Fridrich J. (2012) Steganalysis of JPEG images using rich models. Media Watermarking, Security, and Forensics 2012. DOI: 10.1117/12.907495

Chen M., Sedighi V., Boroumand M. and Fridrich J. (2017) JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images. Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security - IHMMSec '17. DOI: 10.1145/3082031.3083248

Progonov D. (2018) Information-Theoretic Estimations of Cover Distortion by Adaptive Message Embedding. Information Theories and Applications, Vol. 25, No 1, pp. 47-62.

Filler T. and Fridrich J. (2010) Gibbs Construction in Steganography. IEEE Transactions on Information Forensics and Security, Vol. 5, Iss. 4, pp. 705-720. DOI: 10.1109/tifs.2010.2077629

Holub V. and Fridrich J. (2012) Designing steganographic distortion using directional filters. 2012 IEEE International Workshop on Information Forensics and Security (WIFS). DOI: 10.1109/wifs.2012.6412655

Nielsen F. and Nock R. (2014) On the chi square and higher-order chi distances for approximating f-divergences. IEEE Signal Processing Letters, Vol. 21, Iss. 1, pp. 10-13. DOI: 10.1109/lsp.2013.2288355

Huiskes M.J. and Lew M.S. (2008) The MIR flickr retrieval evaluation. Proceeding of the 1st ACM international conference on Multimedia information retrieval - MIR '08. DOI: 10.1145/1460096.1460104

Avcibas I., Memon N. and Sankur B. (2003) Steganalysis using image quality metrics. IEEE Transactions on Image Processing, Vol. 12, Iss. 2, pp. 221-229. DOI: 10.1109/tip.2002.807363

Gonzalez R.C and Woods R. E. (2007) Digital Image Processing, Prentice Hall, 976 p.

Published

2018-12-30

Issue

Section

Information Security