Abstract
The problem of effective counteraction to the malicious data transfer channels is of importance in any area where data transfer is performed. One of the aspects of the Mobile Internet Security is detecting such channels, regardless the way these channels are organized. Steganography is one of the ways to interact without attracting attention, and still digital image is one of the most popular steganographic containers nowadays. The effectiveness of the weighted steganalysis of still digital images in the spatial domain in the task of determining the fact of embedding in the least significant bits of the images with a significant fraction of a homogeneous background is discussed. A definition of the concept of a homogeneous background of a natural digital image in the steganalysis problem is given. The connection of the fraction of a homogeneous background in the image with the efficiency of a weighted steganalysis of the image is shown. The connection between the accuracy of the prediction of the pixel values in the background areas of images and the effectiveness of steganalysis in such is shown. A method for predicting the pixel values of background images has been developed, which makes it possible to improve the efficiency of weighted steganalysis of images with a significant fraction of a homogeneous background by 3–8%. The data of numerical estimates of the increase in the effectiveness of steganalysis are presented using the proposed method for predicting pixel values in background areas.
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This work was supported by Government of Russian Federation (Grant 08-08).
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Bashmakov, D.A., Korobeynikov, A.G., Sivachev, A.V., El Baz, D., Levshun, D. (2019). Method for Predicting Pixel Values in Background Areas in the Problem of Weighted Steganalysis in the Spatial Domain of Natural Images Under Small Payloads. In: You, I., Chen, HC., Sharma, V., Kotenko, I. (eds) Mobile Internet Security. MobiSec 2017. Communications in Computer and Information Science, vol 971. Springer, Singapore. https://doi.org/10.1007/978-981-13-3732-1_5
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