Статья 'Адаптивное предсказание пикселей пикселей в градиентных областях для улучшения точности стеганоанализа в неподвижных цифровых изображениях' - журнал 'Кибернетика и программирование' - NotaBene.ru
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Reference:

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

bashmakov.dan@gmail.com
Другие публикации этого автора
 

 

DOI:

10.25136/2306-4196.2018.2.25514

Review date:

21-02-2018


Publish date:

23-04-2018


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

References
1.
Грибунин В.Г., Оков И.Н., Туринцев И.В. Цифровая стеганография. М.:Солон-Пресс. 2016. 262 с.
2.
Steganography: A Powerful Tool for Terrorists and Corporate Spies // Stratfor [Электронный ресурс]. Режим доступа: https://www.stratfor.com/analysis/steganography-powerful-tool-terrorists-and-corporate-spies, свободный. Яз. англ. (дата обращения 22.08.2017).
3.
Gayathri C., Kalpana V. Study on image steganography techniques // International Journal of Engineering and Technology (IJET). 2013. V. 5. P. 572–577.
4.
Прохожев Н.Н., Михайличенко О.В., Башмаков Д.А., Сивачев А.В., Коробейников А.Г. Исследование эффективности применения статистических алгоритмов количественного стеганоанализа в задаче детектирования скрытых каналов передачи информации // Программные системы и вычислительные методы. 2015. № 3. С. 281–292. doi: 10.7256/2305-6061.2015.3.17233
5.
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. Р. 89–94. doi:10.1007/978-3-319-33816-3_9
6.
Башмаков Д.А., Прохожев Н.Н., Михайличенко О.В., Сивачев А.В. Применение матриц соседства пикселей для улучшения точности стеганоанализа неподвижных цифровых изображений с однородным фоном // Кибернетика и программирование. — 0.-№ 0.-С.0-0. DOI: 10.25136/2306-4196.0.0.24919. URL: http://e-notabene.ru/kp/article_24919.html (Статья ожидает публикации)
7.
BOWS2 the 10 000 original images [Электронный ресурс]. Режим доступа: http://bows2.ec-lille.fr/, свободный. Яз. англ. (дата обращения 12.04.2017).
8.
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
9.
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
10.
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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