Modification of steganalytic histogram method for images with deep distortion

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.

1. Pitolin А.V. Preobrazhenskii Yu.P. Choporov О.N. A study of the possibilities of using steganographic methods of information protection. Modelirovanie, optimizaciya i informacionnie tehnologii = The scientific journal modeling, optimization and information technology. 2018;6(2). URL: https://moit.vivt.ru/wp-content/uploads/2018/04/PitolinSoavtors_2_18_1.pdf (In Russ.). (accessed on 10.12.2022).

2. Russia Patent № 2022620647. Trasologicheskaya i taksonomicheskaya informaciya o steganograficheskih prilojeniyah_ ispolzuyuschih v kachestve konteinerov faili rastrovih graficheskih formatov / R.A. Soloduha, A.A. Volkov, A.G. Kromskih, A.O. Efimov. (In Russ.).

3. Soloduha R.А. Koncepciya formirovaniya sistemy protivodeystviya steganograficheskim kanalam v komputernyh setyah organov vnutrennih del. Vestnik VI MVD Rossii. 2021;1:132–142. (In Russ.).

4. Kawaguchi E., Eason R. Principle and applications of BPCS-steganography. Multimedia Systems and Applications, 1998;3528:464–473.

5. Qtech Hide & View Download Page. URL: http://datahide.org/BPCSe/QtechHV-download-e.html (accessed on 10.12.2022).

6. Image Database BOSSbase 1.01 URL: http://dde.binghamton.edu/download/ImageDB/BOSSbase_1.01.zip (accessed on 10.01.2022).

7. Adelson E. Digital Signal Encoding and Decoding Apparatus. – U.S. Patent. – No. 4939515, 1990.

8. CryptArkan encrypts and hides data files and directories. URL: https://cryptarkan.software.informer.com/download/ (accessed on 10.12.2022).

9. Westfeld A., Pfitzmann A. Attacks on Steganographic Systems: Breaking the Steganographic Utilities EzStego, Jsteg, Steganos and S-Tools-and Some Lessons Learned. 3rd International Workshop on Information Hiding. 2000.

10. Soloduha R.A., Atlasov I.V. Modificaciya metoda pair of values dlya ataki na osnovanii izvestnogo steganovlozeniya. Vestnik VI MVD Rossii. 2012:3:194-200. (In Russ.).

11. Russia Patent № 2022682838. Avtomatizaciya steganograficheskih prilojenii / R.A. Soloduha. (In Russ.).

12. Minaychev А.А., Mezentcev А.О., Yandashevskaya E.А. Development of a steganalysis system for digital images based on a neural network classifier. Modeling, Optimization and Information Technology. 2022:10(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1196 DOI: 10.26102/2310-6018/2022.37.2.020 (In Russ.) (accessed on 10.12.2022).

13. Soloduha R.A., Atlasov I.V., Kubasov I.A. Steganoanaliz cifrovyh izobrazhenij: tekhnologii, algoritmy, programmnaya realizaciya: monografiya. Voronezh: Voronezhskij institut MVD Rossii; 2022. 172 p. (In Russ.).