The relevance of the research is due to the need to counteract hidden data transmission channels in the form of file steganography in institutional and corporate computer networks. The article is devoted to the formation of a feature vector based on the brightness histogram to identify the steganography that distorts several bit planes of the spatial domain in the image. It is assumed that this type of steganography is most likely to be used by inner violator because it does not require deep knowledge in the field of information technology. Additionally, it is implemented in software products of the freeware segment and helps to payload up to 50 % of the container size. A numerical experiment was performed to verify the results. The description of the initial data and the experimental methodology is given. Datasets are obtained by MatLab. To ensure reproducibility of the experiments, the datasets and MatLab scripts are presented in Kaggle. The machine learning procedure based on SVM regression is applied. Based on experimental data, the basic metrics of machine learning effectiveness of feature vectors for BPCS- and LSB-steganalysis are calculated. The dependence of the regression error for feature vectors based on combinations of different bit planes is shown. With the help of the obtained estimates, the analyst can include one features or another in the complex vector.
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Solodukha Roman Aleksandrivoch
Candidate of Technical Sciences, Assistant Professor
Email: standartal@list.ru
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Voronezh, The Russian Federation
