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Подход к извлечению робастного водяного знака из изображений, содержащих текст

Труды СПИИРАН. 2018. Т. 5. № 60. С. 128-155.
Козачок А. В., Копылов С. А., Мещеряков Р. В., Евсютин О. О., Туан Л. М.

This paper presents an approach to a robust watermark extraction from images containing text. Data extraction based on developed approach to robust watermark embedding into text data, characterizing by conversion invariance of text data into an image format. The comparative analysis of existing approaches of steganographic data embedding into text data is carried out, their advantages and disadvantages are determined. The choice of groups to steganographic data embedding methods based on text formatting is justified. As an embedding algorithm is determined approach based on interline space shifting. The block diagram and the description of the developed algorithm of data embedding into text data are given. An experimental estimation of the embedding capacity and perceptual invisibility of the developed data embedding approach was carried out. An approach to extract embedded information from images containing a robust watermark, based on the existing limitations, has been developed. The Radon transform is chosen as the basic extraction procedure of embedded information, allowing to extract values of the interline spacing. An approach based on Gaussian mixture model separating to isolate the values of the bits was chosen. The limits of the retrieval of embedded data have been experimentally established, and the robustness of the developed embedding approach to the implementation of various transformations has been estimated. The following parameters of robustness developed approach are defined: rotation of an image containing embedded data at any angle; scaling an image with a scaling factor not exceeding 1.5; conversion to any bitmap format; the application of a median filter to an image with a convolution core limit of not more than 9, a Gaussian blur filter with a blurring limit not exceeding 8 and an average filter with a convolution kernel limit of not more than 5.