Steganographic resistance of rasterised text-containers to statistical attacks

Authors

  • Pavel P. Urbanovich Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus , The John Paul II Catholic University of Lublin, 14 Racławickie Alley, Lublin 20-950, Poland
  • Margaryta G. Saveleva Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus
  • Nadzeya P. Shutko Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus

Keywords:

steganography, steganographic analysis, attack, rasterisation, halftone shades, steganographic resistance, software
Supporting Agencies
The research was carried out with the financial support of the Government of the Republic of Belarus within the framework of the research project «Methods, algorithms, and software for placing invisible identification information in electronic cards and text documents based on steganography and redundant coding».

Abstract

The resistance of rasterised text stego-containers with hidden information placed in them to the main types of statistical attacks (steganalysis efficiency) is analysed. This embedding is performed by stegаnographic modification of halftone shades of individual pixels that form text symbols. The analysis is carried out using well-known and specially developed copyright software, which implements some well-known, as well as previously proposed by the authors of this article methods of direct and reverse steganographic transformation of rasterised text documents-containers based on the RGB model and on the basis of modification of halftone shades. For comparative assessment and analysis of steganographic resistance to attacks, various types of text stego-containers are tested using most well-known statistical steganalysis methods, such as χ-square attack, regular singular analysis, sample pair attack. It is established that all the listed methods for implementing attacks on rasterised text stego-containers (rasterised text images) in PNG format give a relatively (in relation to rasterised images in the same format) high level of «false alarm»: they show the presence of embedded information in the original (empty) document. The most painful error is given by the χ-square attack. The other two steganalysis methods have approximately the same accuracy. The developed tools for implementing attacks on stego-containers allow solving a range of practical problems related to the analysis, identification and assessment of the security of hidden information in raster images. By simulating various attacks (visual, statistical, attacks with a known source image and stego-keys), it is possible to identify the weaknesses of the created steganographic algorithms.

Author Biographies

  • Pavel P. Urbanovich, Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus, The John Paul II Catholic University of Lublin, 14 Racławickie Alley, Lublin 20-950, Poland

    doctor of science (engineering), full professor; professor at the department of information systems and technologies, faculty of information technologies, Belarusian State Technological University, and professor, The John Paul II Catholic University of Lublin

  • Margaryta G. Saveleva, Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus

    assistant at the department of information systems and technologies, faculty of information technologies

  • Nadzeya P. Shutko, Belarusian State Technological University, 13a Sviardlova Street, Minsk 220006, Belarus

    PhD (engineering), docent; associate professor at the department of informatics and web-design, faculty of information technologies

References

  1. Admass WS, Munaye YY, Diro AA. Cyber security: state of the art, challenges and future directions. Cyber Security and Applications. 2024;2:100031. DOI: 10.1016/j.csa.2023.100031.
  2. Urbanowicz P, Chourikov K, Rimorev A, Urbanovich N. Text steganography application for protection and transfer of the information. Przegląd Elektrotechniczny. 2010;86(7):95– 97.
  3. Evsutin O, Melman A, Meshcheryakov R. Digital steganography and watermarking for digital images: a review of current research directions. IEEE Access. 2020;8:166589–166611. DOI: 10.1109/ACCESS.2020.3022779.
  4. Blinova EA, Urbanovich PP. Steganographic method based on hidden messages embedding into Bezier curves of SVG images. Journal of the Belarusian State University. Mathematics and Informatics. 2021;3:68–83. DOI: 10.33581/2520-6508-2021-3-68-83.
  5. Blinova EA, Stashevskaya IYu, Urbanovich PP. A steganographic method of embedding an identifier into the spatial data of an electronic map. Journal of the Belarusian State University. Mathematics and Informatics. 2023;1:76–87.
  6. Zainal N, Hoshi AR, Ismail M, Rahem RT, Wadi SM. A hybrid steganography and watermark algorithm for copyright protection by using multiple embedding approaches. Bulletin of Electrical Engineering and Informatics. 2024;13(3):1877–1896. DOI: 10.11591/eei.v13i3.6337.
  7. Dalal M, Juneja M. Steganography and steganalysis (in digital forensics): a cybersecurity guide. Multimedia Tools and Applications. 2021;80:5723–5771. DOI: 10.1007/s11042-020-09929-9.
  8. Fernandes CS. Steganography and computer forensics – the art of hiding information: a systematic review. ARIS2-Journal. 2022;2(2):31–38. DOI: 10.56394/aris2.v2i2.20.
  9. Urbanovich P, Shutko N. Theoretical model of a multi-key steganography system. In: Zapala AM, editor. Recent developments in mathematics and informatics. Contemporary mathematics and computer science. Part 2, Computer science. Lublin: Wydawnictwo KUL; 2016. p. 181–202.
  10. Belhamra MA, Souidi EM. Steganography over redundant residue number system codes. Journal of Information Security and Applications. 2020;51:102434. DOI: 10.1016/j.jisa.2019.102434.
  11. Hassan S, Muztaba MA, Hossain MS, Narman HS. A hybrid encryption technique based on DNA cryptography and steganography. In: Institute of Electrical and Electronics Engineers. 2022 IEEE 13 th Annual information technology, electronics and mobile communication conference (IEMCON); 2022 October 12–15; Vancouver, Canada. Vancouver: Institute of Electrical and Electronics Engineers; 2022. p. 501–508. DOI: 10.1109/IEMCON56893.2022.9946512.
  12. Majeed MA, Sulaiman R, Shukur Z. New text steganography technique based on multilayer encoding with format-preserving encryption and Huffman coding. International Journal of Advanced Computer Science and Applications. 2022;13(12):163–172. DOI: 10.14569/IJACSA.2022.0131222.
  13. Yao Y, Wang J, Chang Q, Ren Y, Meng W. High invisibility image steganography with wavelet transform and generative adversarial network. Expert Systems with Applications. 2024;249:123540. DOI: 10.1016/j.eswa.2024.123540.
  14. Lou D-C, Hu C-H. LSB steganographic method based on reversible histogram transformation function for resisting statistical steganalysis. Information Sciences. 2012;188:346–358. DOI: 10.1016/j.ins.2011.06.003.
  15. Agarwal M. Text steganographic approaches: a comparison. arXiv:1302.2718 [Preprint]. 2013 [cited 2025 July 9]: [16 p.]. Available from: https://arxiv.org/abs/1302.2718.
  16. Pejaś J, Cierocki Ł. Reversible data hiding scheme for images using gray code pixel value optimization. Procedia Computer Science. 2021;192:328–337. DOI: 10.1016/j.procs.2021.08.034.
  17. Saveleva MG, Urbanovich PP. Method of steganographic transformation of web-documents based on raster graphics and RGB model. Proceedings of BSTU. Issue 3, Physics and Mathematics. Informatics. 2022;2:99–107. Russian. DOI: 10.52065/2520-6141-2022-260-2-99-107.
  18. Saveleva MG, Urbanovich PP. Steganographic transformation based on the modification of halftone shades of rasterized documents. Information and Control Systems. 2024;6:2–14. Russian. DOI: 10.31799/1684-8853-2024-6-2-14.
  19. Evsutin O, Melman A, Meshcheryakov R. Digital steganography and watermarking for digital images: a review of current research directions. IEEE Access. 2020;8:166589–166611. DOI: 10.1109/ACCESS.2020.3022779.
  20. Hou D, Wang H, Zhang W, Yu N. Reversible data hiding in JPEG image based on DCT frequency and block selection. Signal Processing. 2018;148:41–47. DOI: 10.1016/j.sigpro.2018.02.002.
  21. Shumskaya OO, Zhelezny M. Adaptive algorithm of replacement-based embedding of data into compressed JPEG images. Information and Control Systems. 2018;5:44–56. Russian. DOI: 10.31799/1684-8853-2018-5-44-56.
  22. Liu J, Ke Y, Zhang Z, Lei Y, Li J, Zhang M, et al. Recent advances of image steganography with generative adversarial networks. IEEE Access. 2020;8:60575–60597. DOI: 10.1109/ACCESS.2020.2983175.
  23. Westfeld A, Pfitzmann A. Attacks on steganographic systems breaking the steganographic utilities EzStego, Jsteg, Steganos, and S-Tools – and some lessons learned. In: Pfitzmann A, editor. Information hiding. Berlin: Springer; 2000. p. 61–76 (Lecture notes in computer science; volume 1768).
  24. Dumitrescu S, Wu X, Wang Z. Detection of LSB steganography via sample pair analysis. In: Petitcolas FAP, editor. Information hiding. Berlin: Springer; 2003. p. 355–372 (Lecture notes in computer science; volume 2578).
  25. Luo X, Yang C, Liu F. Equivalence analysis among DIH, SPA, and RS steganalysis methods. In: Leitold H, Markatos EP, editors. Communications and multimedia security (CMS 2006). Proceedings of the 10th IFIP International conference; 2006 October 19–21; Heraklion Crete, Greece. Berlin: Springer; 2006. p. 355–372 (Lecture notes in computer science; volume 4237). DOI: 10.1007/11909033_15.
  26. Karampidis K, Kavallieratou E, Papadourakis G. A review of image steganalysis techniques for digital forensics. Journal of Information Security and Applications. 2018;40:217–235. DOI: 10.1016/j.jisa.2018.04.005.
  27. Vilkhovskiy DE. A survey of steganalysis methods in the papers of foreign authors. Mathematical Structures and Modeling. 2020;4:75–102. Russian. DOI: 10.24147/2222-8772.2020.4.75-102.
  28. Kharin Yu, Vecherko EV. Statistical estimation of parameters for binary Markov chain models with embeddings. Discrete Mathematics and Applications. 2013;23(2):153–169. DOI: 10.1515/dma-2013-009.
  29. Apau R, Asante M, Twum F, Ben Hayfron-Acquah J, Peasah KO. Image steganography techniques for resisting statistical steganalysis attacks: a systematic literature review. PLoS ONE. 2024;19(9):0308807. DOI: 10.1371/journal.pone.0308807.
  30. Alqahtani J, Alghazzawi D, Cheng L. Steganalysis algorithm for PNG images based on fuzzy logic technique. International Journal of Network Security & Its Applications. 2016;8(6):1–16. DOI: 10.5121/ijnsa.2016.8601.
  31. Farooq N, Selwal A. Image steganalysis using deep learning: a systematic review and open research challenges. Journal of Ambient Intelligence and Humanized Computing. 2023;14:7761–7793. DOI: 10.1007/s12652-023-04591-z.
  32. Aljarf A, Zamzami H, Gutub A. Integrating machine learning and features extraction for practical reliable color images steganalysis classification. Soft Computing. 2023;27(19):13877–13888. DOI: 10.1007/s00500-023-09042-7.
  33. Fridrich J, Goljan M, Du R. Detecting LSB steganography in color, and gray-scale images. IEEE MultiMedia. 2001;8(4):22–28. DOI: 10.1109/93.959097.
  34. Urbanovich PP, Shutko NP. Usage of hypercomplex numbers in a cryptographic key agreement protocol based on neural networks. Journal of the Belarusian State University. Mathematics and Informatics. 2024;2:81–92. EDN: MIPTIK.

Downloads

Additional Files

Published

2026-09-25

Issue

No. 2 (2026): Journal of the Belarusian State University. Mathematics and Informatics

Section

Theoretical Foundations of Computer Science

License

Copyright (c) 2026 Journal of the Belarusian State University. Mathematics and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The authors who are published in this journal agree to the following:

  1. The authors retain copyright on the work and provide the journal with the right of first publication of the work on condition of license Creative Commons Attribution-NonCommercial. 4.0 International (CC BY-NC 4.0).
  2. The authors retain the right to enter into certain contractual agreements relating to the non-exclusive distribution of the published version of the work (e.g. post it on the institutional repository, publication in the book), with the reference to its original publication in this journal.
  3. The authors have the right to post their work on the Internet (e.g. on the institutional store or personal website) prior to and during the review process, conducted by the journal, as this may lead to a productive discussion and a large number of references to this work. (See The Effect of Open Access.)

How to Cite

[1]
Urbanovich, P. et al. 2026. Steganographic resistance of rasterised text-containers to statistical attacks. Journal of the Belarusian State University. Mathematics and Informatics. 2 (Sep. 2026), 131–141.