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Application of neighborhood matrices of pixels to improve the accuracy of steganoanalysis of fixed digital images with a homogeneous background
Bashmakov Daniil Andreevich

graduate student, Department of Design and Security of Computer Systems, St. Petersburg National Research University of Information Technologies, Mechanics and Optics

197101, Russia, Saint Petersburg, ul. Kronverskii, 49, of. 474

basme@list.ru
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Prokhozhev Nikolai Nikolaevich

PhD in Technical Science

Associate Professor, St. Petersburg National Research University of Information Technologies, Mechanics and Optics

197101, Russia, g. Saint Petersburg, ul. Kronverskii, 49, of. 474

19791109@list.ru
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Mikhailichenko Ol'ga Viktorovna

Senior Lecturer, Department of Design and Security of Computer Systems, St. Petersburg National Research University of Information Technologies, Mechanics and Optics

197101, Russia, Sankt-Peterburg, Kronverkskii prospekt, d.49

19791109@list.ru
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Sivachev Aleksei Vyacheslavovich

graduate student, Department of Design and Security of Computer Systems, St. Petersburg National Research University of Information Technologies, Mechanics and Optics

197101, Russia, Saint Petersburg, Kronverskii prospekt, 49, of. 474

sivachev239@mail.ru
Другие публикации этого автора
 

 

Abstract.

The article consideres the accuracy of the steganoanalysis using the Weighted Stego algorithm in passive data transmission channel countermeasures using the method of embedding the spatial region of fixed digital images with the RGB color model into the smallest significant bit. The dependence of the accuracy of steganoanalysis by the Weighted Stego method on the fraction of a homogeneous background in the analyzed image was studied. The drop in the accuracy of pixel prediction in the background areas of the image is investigated using the prediction model proposed by the authors of the original Weighted Stego algorithm. The Weighted Stego steganoanalysis algorithm is investigated. The basis for the steganoanalysis algorithm is a model for predicting the pixel values of the analyzed image from adjacent pixels. To assess the effectiveness of the analysis, the BOWS2 collection was used. Embedding information is realized by changing the least significant bits of the image in the spatial domain with a payload of 3-5 %%. The effectiveness of the methods is determined taking into account the obtained truly positive, truly negative, false positive and false negative values of the classification of images. The fall in the accuracy of steganoanalysis by the Weighted Stego method is shown with an increase in the fraction of a homogeneous background in the analyzed image. The method of improvement of the pixel prediction model in the basis of Weighted Stego is proposed, which allows to level out the drop in accuracy with increasing the fraction of a homogeneous background in the analyzed image. The results of the work are useful to a specialist in the field of information protection in the tasks of detecting and countering a hidden data channel. The obtained results can be used in the development of steganoanalysis systems based on the Weighted Stego algorithm.

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

DOI:

10.25136/2306-4196.2018.1.24919

Article was received:

08-12-2017


Review date:

25-12-2017


Publish date:

27-02-2018


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

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