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Cybernetics and programming

Adaptive Prediction of Pixels in Gradient Areas to Raise Steganalysis Accuracy of Static Digital Images
Bashmakov Daniil Andreevich

post-graduate student of the Department of Computing System Design and Safety at ITMO University (Saint Petersburg National Research University of Information Technologies, Mechanics and Optics)

197101, Russia, Leningradskaya oblast', g. Saint Petersburg, Kronverkskii prospekt, 49

Другие публикации этого автора



In his research Bashmakov analyzes accuracy of background area selection in static digital images by using the histogram method as part of steganalysis performed by Weighted Stego Image and WSPAM methods. He examines the dependence of practical accuracy of steganalysis of static digital images by using Weighted Stego Image and  WSPAM methods on the kind of prediction model in gradient regions of an image as part of resistance to data transmission channels that use the method of embedding the least significant bit of spatial domain in static digital images with a significant part of homogeneous background. The author analyzes the Weighted Stego steganalysis algorithm and WSPAM modification thereof. To evaluate the analysis efficiency, the author has used the BOWS2 collection. To evaluate efficiency of homogenous background selection, the author has used images selected from a wide range of sources. The information is built in by changing the least significant bits of images in spatial domain with an actual load from 3-5%. Efficiency of methods is defined based on true-positive, true-negative, false-positive and false-negative values of image classification. The author demonstrates the low accuracy of homogenous background selection using the histogram method. The author suggests to select homogenous background using the segmentation neural net and proves its efficiency. He also offers an improved model of pixel prediction in image gradient areas, this model allowing to achieve the highest accuracy of steganalysis. The results of the research can be used to create systems of passive resistance to steganographic data transmission channels that are based on the Weighted Stego algorithm. 

Keywords: steganalytic algorithm, steganographic embedding, steganalysis method accuracy, image spatial domain, statistical steganalysis, passive resistance, least significant bit, binary classification, steganalysis, steganography



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This article written in Russian. You can find full text of article in Russian here .

Gribunin V.G., Okov I.N., Turintsev I.V. Tsifrovaya steganografiya. M.:Solon-Press. 2016. 262 s.
Steganography: A Powerful Tool for Terrorists and Corporate Spies // Stratfor [Elektronnyi resurs]. Rezhim dostupa: https://www.stratfor.com/analysis/steganography-powerful-tool-terrorists-and-corporate-spies, svobodnyi. Yaz. angl. (data obrashcheniya 22.08.2017).
Gayathri C., Kalpana V. Study on image steganography techniques // International Journal of Engineering and Technology (IJET). 2013. V. 5. P. 572–577.
Prokhozhev N.N., Mikhailichenko O.V., Bashmakov D.A., Sivachev A.V., Korobeinikov A.G. Issledovanie effektivnosti primeneniya statisticheskikh algoritmov kolichestvennogo steganoanaliza v zadache detektirovaniya skrytykh kanalov peredachi informatsii // Programmnye sistemy i vychislitel'nye metody. 2015. № 3. S. 281–292. doi: 10.7256/2305-6061.2015.3.17233
Prokhozhev N., Mikhailichenko O., Sivachev A., Bashmakov D., Korobeynikov A.G. Passive Steganalysis Evaluation: Reliabilities of Modern Quantitative Steganalysis Algorithms // Advances in Intelligent Systems and Computing. 2016. V. 451. R. 89–94. doi:10.1007/978-3-319-33816-3_9
Bashmakov D.A., Prokhozhev N.N., Mikhailichenko O.V., Sivachev A.V. Primenenie matrits sosedstva pikselei dlya uluchsheniya tochnosti steganoanaliza nepodvizhnykh tsifrovykh izobrazhenii s odnorodnym fonom // Kibernetika i programmirovanie. — 0.-№ 0.-S.0-0. DOI: 10.25136/2306-4196.0.0.24919. URL: http://e-notabene.ru/kp/article_24919.html (Stat'ya ozhidaet publikatsii)
BOWS2 the 10 000 original images [Elektronnyi resurs]. Rezhim dostupa: http://bows2.ec-lille.fr/, svobodnyi. Yaz. angl. (data obrashcheniya 12.04.2017).
J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3431-3440. doi: 10.1109/CVPR.2015.7298965
Hongyuan Zhu, Fanman Meng, Jianfei Cai, Shijian Lu, Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation, Journal of Visual Communication and Image Representation, Volume 34, 2016, Pages 12-27, ISSN 1047-3203, https://doi.org/10.1016/j.jvcir.2015.10.012
Ker, Andrew. (2007). A Weighted Stego Image Detector for Sequential LSB Replacement. Proceedings-IAS 2007 3rd Internationl Symposium on Information Assurance and Security. 453-456. 10.1109/IAS.2007.71.
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