Eng During last 365 days Approved articles: 2094,   Articles in work: 307 Declined articles: 830 
Articles and journals | Tariffs | Payments | Your profile

Prokhozhev N.N., Mikhaylichenko O.V., Bashmakov D.A., Sivachev A.V., Korobeynikov A.G. Study the effectiveness of statistical algorithms of quantitative steganalysis in the task of detecting hidden information channels

Published in journal "Software systems and computational methods", 2015-3 in rubric "Data encryption and data protection", pages 281-292.

Resume: Countering the hidden channels of information transmission is an important task in the organization of information security. One kind of passive physical resistance methods is detection of the steganographic impact on the investigated container. The widespread use of digital still images as stegano-containers is due to their large share in total data traffic. The task of passive counteraction (steganalysis) allowing identifying the digital image with the built-in information is actually a binary classification problem. At the core of the classifier lies statistical algorithm of quantitative steganalysis for determining the amount of modified pixels in the data container. The accuracy of the algorithm directly affects the quality classification and the practical effectiveness of passive physical resistance as a whole. By effective counteraction the article refers to the ratio of probabilities between true positive classification and the probability of a false positive classification. Currently there are many statistical algorithms for quantitative steganalysis. However, there are no studies on their comparative analysis which complicates the selection of an algorithm while solving the problem of counteraction to steganography channels of information leakage. The practical effectiveness of passive physical resistance to steganography channels by inserting the least significant bits of pixel digital image also remains an open question. The subject of the study is the effectiveness of the application of modern quantitative statistical algorithms steganalysis. Based on the results of the study the authors have formed graphics of trust regions, allowing to make a comparative assessment of the effectiveness of the passive counteraction in LSB-steganography. For the study the authors selected the following steganalysis algorithms: RS- analysis, Sample pair analysis, Difference image histogram, Triples analysis, Weighted stego-image. From the test of multiple images an image is selected. An evaluation of its capacity (defined by the maximum payload) is performed. In the experiments for this value authrs accepted the total number of pixels in the image. Steganographic effects modeled by changing the value of the least significant bit for a predetermined number of pixels (the payload). The modified image used as an input to a particular implementation of the algorithm steganalysis. The result of the algorithm is the number of changed pixels in the image. The experiments were carried out under the same conditions for all implementations of algorithms steganalysis. The main conclusions of the study is the fact that based on modern statistical steganalysis algorithms it is possible to organize an effective opposition to the passive channels with LSB steganography with embedding payload container more than 5%. Reducing the payload container of less than 5% dramatically reduces the effectiveness of the passive counteraction. A small 600x400 pixels image converted to steganography with payload of 1-2% is practically not detected by classifiers based on statistical quantitative algorithms steganalysis. Taking into account the possibility of pre-compression hidden data and matrix embedding, the considered modern algorithms for steganalysis need further improvement.

Keywords: the steganalysis algorithm, weighted stego-image, difference histogram analysis, simple pair analysis, LSB-based steganography, statistical quantative steganalysis, steganography, digital watermark, still images, statistical analysis algorithms

DOI: 10.7256/2305-6061.2015.3.17233

This article can be downloaded freely in PDF format for reading. Download article

Korobeynikov A.G., Kutuzov I.M. Algoritm obfuskatsii // Kibernetika i programmirovanie. - 2013. - 3. - C. 1 - 8. DOI: 10.7256/2306-4196.2013.3.9356. URL: http://www.e-notabene.ru/kp/article_9356.html
H.G. Schaathun. Machine learning in image steganalysis // Alesund University College, pp 164-167 , 2012
Y. Kim Z. Duric, D. Richards, Modified matrix encoding technique for minimal distortion steganography// in Proc. 8th Int. Workshop Information Hiding. Jul. 10-12, 2006, vol. 4437, pp. 314-327
Kollektsiya Fotografii Ameriki 20-go veka [Elektronnyy resurs] / Spisok forumov rutracker.org Rezhim dostupa: http://rutracker.org/forum/viewtopic.php?t=3501973 (Data obrashcheniya: 30.05.2014)
Kollektsiya "World in photo" [Elektronnyy resurs] / Spisok forumov rutracker.org Rezhim dostupa: http://rutracker.org/forum/viewtopic.php?t=2272897 (Data obrashcheniya: 30.05.2014)
Fotografii gribov [Elektronnyy resurs] / Spisok forumov rutracker.org Rezhim dostupa: http://rutracker.org/forum/viewtopic.php?t=3705334 (Data obrashcheniya: 30.05.2014)
Mao Ye, Fenlin Liu, Chunfang Yang, Xiongfei He Steganalysis Based on Weighted Stego-Image for LSB Replacement Steganography// Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. pp. 945-948
A.D. Ker: A general framework for structural steganalysis of LSB Replacement // Proc. of the Information Hiding, pp.296-311, 2005.
Lu, P., X. Luo et. al.// An improved sample pairs method for detection of LSB embedding, Proc. of the 6th Information Hiding Workshop, Springer LNCS, vol.3200, pp.116-128, 2004
Zhang T. and X. Ping // Reliable detection of LSB steganography based on the difference image histogram, Proc. of the IEEE ICSAAP 2003, Part III, pp. 545-548, 2003.
J. Fridrich, M. Goljana, D. Soukalb Higher-order statistical steganalysis of palette images, Department of Electrical and Computer Engineering, Department of Computer Science, SUNY Binghamton, Binghamton, NY 13902-6000
J.Fridrich, M.Goljan, R.Du Reliable Detection of LSB Steganography in Color and Grayscale Images, State Univ. of New York, Binghamton, NY, USA.
A. Nissar, A.H. Mir Classification of steganalysis techniques, Digital Signal Processing 20 (2010) pp. 17581770
L. Singh, R. Chhikara A Review on Digital Image Steganalysis Techniques Categorised by Features Extracted, ITM University, Gurgaon, Haryana, India. International Journal of Engineering and Innovative Technology (IJEIT)Volume 3, Issue 4, October 2013
J. Kelley Terrorist instructions hidden online [Elektronnyy resurs] / USA TODAY, Virginia, USA. Rezhim dostupa: http://usatoday30.usatoday.com/tech/news/2001-02-05-binladen-side.htm (Data obrashcheniya: 30.05.2014)
S. Gallagher Steganography: how al-Qaeda hid secret documents in a porn video [Elektronnyy resurs] / Ars Technica, Boston, USA. Rezhim dostupa: http://arstechnica.com/business/2012/05/steganography-how-al-qaeda-hid-secret-documents-in-a-porn-video (Data obrashcheniya: 30.05.2014)
S. Fox FBI: Russian spies hid codes in online photos [Elektronnyy resurs] // NBC News, New York, USA. Rezhim dostupa: http://www.nbcnews.com/id/38028696/ns/technology_and_science/t/fbi-russian-spies-hid-codes-online-photos (Data obrashcheniya: 30.05.2014)
Gribunin V.G. Tsifrovaya steganografiya [Tekst] : monografiya // V.G. Gribunin, I.N. Okov, I.V. Turintsev. M. : SOLON-Press, 2002. 272 s.
Konakhovich, G.F. Komp'yuternaya steganografiya [Tekst]: teoriya i praktika //G.F. Konakhovich, A.Yu. Puzyrenko. Kiev : MK-Press, 2006. 288 s.

Correct link to this article:
just copy this link to clipboard