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Accuracy of Pixel Predication in Background Areas of Digital Images as Part of Stegoanalysis Performed Using the Weighted Stego Method
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





 The subject of the research is the accuracy of predicting pixels of background areas of static digital images using the Weighted Stego Image method as part of passive resistance to data transmission channels that use the method of embedding the spatial domain of static digital images with a significant share of homogeneous background in the least significant bit. In his research Bashmakov analyzes the dependence of the accuracy of defining the length of an embedded message on the accuracy of pixel prediction of the image background area. The author focuses on an original algorithm Weighted Stego Image and a number of modifications thereof including the AWSPAM version. He analyzes the formula for calculating an embedded message length and the relationship between its accuracy and the accuracy of pixel prediction. The pixel prediction accuracy is viewed as an error distribution between the predicted and actual values. The accuracy of the stegoanalysis algorithm is viewed as the share of false-positive classifications given the share of correct classifications. The author demonstrates that information about surrounding pixels is not enought for an accurate prediction of pixel and proves that there is a certain relationship between the accuracy of pixel predition and accuracy of an embedded message length, or, in other words, accuracy of the classification using the Weighted Stego method. The author also demonstrates that the AWSPAM algorithm can predict pixels with the highest accuracy compared to the original algorithm prediction function and other modifications thereof.

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



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