2021, issue 1, p. 74-85

Received 04.03.2021; Revised 11.03.2021; Accepted 25.03.2021

Published 30.03.2021; First Online 03.04.2021

https://doi.org/10.34229/2707-451X.21.1.8

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MSC 68M25, 68P27

About JPEG Images Parameters Impact to Steganalys Accuracy

N. Koshkina ORCID ID favicon Big

V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv

Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Introduction. Existing examples of illegal use of computer steganography prove the need for the development of stegananalytical methods and systems as one of the most important areas of cybersecurity. The advantage of machine learning-based stegananalytical methods is their versatility: they do not rely on knowledge of the injection algorithm and can be used to detect a wide range of steganographic methods. However, before being used for detecting steganocontainers, the methods mentioned require training on containers that are determined for sure whether they contain hidden messages or not. On this stage, it is very important to understand how the parameters of containers under investigation, in particular, such a common variant as JPEG images, affect the accuracy of steganalysis. After all, the inconsistency of the source of containers is an open problem of steganalysis leading to significant decrease of accuracy of detecting hidden messages after the classifier is moved from the laboratory to the real world.

The purpose of the work is investigation of influence of the content, size and quality factor of JPEG images to the accuracy of their steganalysis performed by statistical methods based on machine learning.

Results. During the research the following patterns were revealed: 1) the accuracy is better when images with a close percentage of coefficients suitable for DCT concealment are used for training and control, 2) images are classified more accurately when they have a relatively small number of suitable DCT coefficients, 3) with using mixed training samples (by content or parameters) the accuracy of steganalysis deteriorates, 4) decreasing quality factor of JPEG-images leads to increasing the accuracy of their steganalysis, 5) increasing size of images increases the accuracy of their steganalysis, 6) images where desynchronization of blocks took place during preprocessing are classified more accurately, 7) the sequence of the image preprocessing operations affects the accuracy of its steganoanalysis.

Conclusions. For steganography tasks the choice of JPEG containers, taking into account revealed patterns, makes steganographic hides more resistant to passive attacks. Considering them for tasks of steganalysis allows one to interpret the obtained results more accurately.

 

Keywords: information security, steganography, stegananalysis, intelligent computer systems, machine learning, detection accuracy.

 

Cite as: Koshkina N. About JPEG Images Parameters Impact to Steganalys Accuracy. Cybernetics and Computer Technologies. 2021. 1. P. 74–85. (in Ukrainian) https://doi.org/10.34229/2707-451X.21.1.8

 

References

           1.     Holotyak T., Fridrich J., Voloshynovskyy, S. Blind statistical steganalysis of additive steganography using wavelet higher order statistics. Communications and Multimedia Security, 9th IFIP TC-6 TC-11 International Conference. 2005. P. 273 274. https://doi.org/10.1007/11552055_31

           2.     Pevny T., Bas P., Fridrich J. Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on information Forensics and Security. 2010. 5 (2). P. 215 224. https://doi.org/10.1145/1597817.1597831

           3.     Ker A. Steganalysis of LSB matching in grayscale images. IEEE Signal Processing Letters. 2005. 12 (6). P. 441–444. https://doi.org/10.1109/LSP.2005.847889

           4.     Huang F., Shi Y.Q., Huang J. New JPEG steganographic scheme with high security performance. International Workshop on Digital Watermarking. 2010. 6526. P. 189–201. https://doi.org/10.1007/978-3-642-18405-5_16

           5.     Pevny T., Bas P., Fridrich J. Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on information Forensics and Security. 2010. 5 (2). P. 215–224. https://doi.org/10.1109/TIFS.2010.2045842

           6.     Xia Z., Wang X., Sun X., Wang B. Steganalysis of least significant bit matching using multi-order differences. Security and Communication Networks. 2014. 7 (8). P.1283–1291. https://doi.org/10.1002/sec.864

           7.     Zeng J., Tan S., Li B., Huang J. Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework. IEEE Transactions on Information Forensics and Security. 2018. 13 (5). Р. 1200–1214. https://doi.org/10.1109/TIFS.2017.2779446

           8.     Mustafa E.M., Elshafey M.A., Fouad M.M. Enhancing CNN-based Image Steganalysis on GPUs. Journal of Information Hiding and Multimedia Signal Processing. 2020. 11 (3). Р.138–150. https://www.researchgate.net/publication/344140896_Enhancing_CNN-based_Image_Steganalysis_on_GPUs

           9.     Boroumand M., Chen M., Fridrich J. Deep Residual Network for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security. 2019. 14 (5). P. 1181–1193. https://doi.org/10.1109/TIFS.2018.2871749

       10.     Kodovsky J., Fridrich J. Steganalysis in high dimensions: fusing classifiers built on random subspaces. Proc. SPIE, Electronic Imaging, Media, Watermarking, Security and Forensics XIII. 2011. 7880 (78800L). https://doi.org/10.1117/12.872279

       11.     Fridrich J., Kodovsky J. Rich Models for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security. 2012. 7 (3). Р. 868–882. https://doi.org/10.1109/TIFS.2012.2190402

       12.     Holub V., Fridrich J. Random Projections of Residuals for Digital Image Steganalysis. IEEE Transactions on Information Forensics and Security. 2013. 8 (12). Р. 1996 2006. https://doi.org/10.1109/TIFS.2013.2286682

       13.     Holub V., Fridrich J. Phase-Aware Projection Model for Steganalysis of JPEG Images. Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XVII. 2015. 9409. https://doi.org/10.1117/12.2075239  

       14.     Li W., Zhou W., Zhang W., Qin C., Hu H., Yu N. Shortening the Cover for Fast JPEG Steganography. IEEE Transactions on Circuits and Systems for Video Technology. 2020. 30 (6). P. 1745–1757. https://doi.org/10.1109/TCSVT.2019.2908689

       15.     Progonov D. Statistical Steganalysis of Multistage Embedding Methods. International Journal “Information Models & Analyses”. 2016. 5 (1). Р. 23–36.

       16.     Yang Y., Kong X., Wang B., Ren K., Guo Y. Steganalysis on Internet images via domain adaptive classifier Neurocomputing. 2019. 351. P. 205–216. https://doi.org/10.1016/J.NEUCOM.2019.04.025

       17.     Korolov V.Iu., Polinovskyi V.V., Herasymenko V.A., Horynshtein M.L. On the results of researches of statistical properties of color images according to the method RS-stegoanalysis. Informatsiia i pravo. 2011. 3 (3). P. 102–110. (in Ukrainian) http://dspace.nbuv.gov.ua/handle/123456789/39035

       18.     Kharrazi M., Sencar H.T., Memon N.D. Performance study of common image steganography and steganalysis techniques. Journal of Electronic Imaging. 2006. 15 (4). Р. 041104-1–16. https://doi.org/10.1117/1.2400672

       19.     Holub V., Fridrich J., Denemark T. Universal Distortion Function for Steganography in an Arbitrary Domain. EURASIP Journal on Information Security. 2014. 1. P. 1–13. https://doi.org/10.1186/1687-417X-2014-1

       20.     Kodovský J., Fridrich J., Holub V. Ensemble Classifiers for Steganalysis of Digital Media. IEEE Transactions on Information Forensics and Security. 2012. 7 (2). P. 432–444. https://doi.org/10.1109/TIFS.2011.2175919

       21.     Koshkina N.V. Research of main components of machine learning based JPEG-stegananalysis systems. Ukrainian Information Security Research Journal. 2020. 22 (2). С. 97–108. (in Ukrainian)  http://193.178.34.32/index.php/ZI/article/view/14801/21490

       22.     Song X., Liu F., Yang C., Luo X., Zhang Y. Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters. Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security. ACM. 2015. P. 1523. https://doi.org/10.1145/2756601.2756608

       23.     Holub V., Fridrich J. Low Complexity Features for JPEG Steganalysis Using Undecimated DCT. IEEE Transactions on Information Forensics and Security. 2015. 10 (2). P. 219 228. https://doi.org/10.1109/TIFS.2014.2364918

       24.      Koshkina N.V. Comparison of Efficiency of Statistical Models Used for Formation of Feature Vectors by JPEG Images Steganalysis. Theoretical and Applied Cybersecurity. 2 (2). P. 22–28. https://doi.org/10.20535/tacs.2664-29132020.1.209433

 

 

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