Алгоритм выявления стеганографических вставок типа LSBзамещения на основе метода анализа иерархий
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

An algorithm for identifying steganographic inserts of the LSB-replacement type based on the hierarchy analysis

Guts A.K.   Vilkhovskiy D.E.  

UDC 004.932.2
DOI: 10.26102/2310-6018/2020.29.2.006

Публикация отозвана (RETRACTED) 25.12.2020
Причина ретракции: дублирование публикации. Дополнительная информация о причинах ретракции: статья является результатом перефразирования ранее опубликованной статьи без ссылки на одного из соавторов исходной статьи (Белим С.В., Вильховский Д.Э. Алгоритм выявления стеганографических вставок типа LSB-замещения на основе метода анализа иерархий // Вестник компьютерных и информационных технологий. 2018. № 4. С. 25-33).
  • Abstract
  • List of references
  • About authors

The article proposes an algorithm for identifying steganographic inserts implemented as a replacement of the least significant bits. The proposed algorithm is based on the hierarchy analysis method. The layers of the least significant bits of the blue component are considered. The embedding areas are determined using the taxonomy algorithm. A preprocessing algorithm is applied in order to increase efficiency in areas that contain gradient fill. The scientific novelty lies in the development of an algorithm for steganographic analysis of the LSB replacement method with low filling of the container, based on a comparative analysis of several image layers using the hierarchy analysis method, characterized in that the selected decision criteria provide the opportunity to take into account the structure of the original container image that is stored in higher bit layers and due to this it is possible to form a map of suspicious pixels, increasing efficiency embedded messages.A computer experiment was performed. For artificial images with gradient and uniform fill, the proposed algorithm makes it possible to determine on average 91% of the replaced bits, while false positives are no more than 1%. The position of the embedded bits can be determined by matching the decision matrix with the initial image.The proposed algorithm is effective for the small size of the embedded message, in contrast to the previously created algorithms.

1. Adelson E. Digital Signal Encoding and Decoding Apparatus. U.S. Patent. 1990. N. 4,939515.

2. Westfeld A., Pfitzmann A. Attacks on Steganographic Systems: Breaking the Steganographic Utilities EzStego, Jsteg, Steganos and Stools and Some Lessons Learned. 3rd International Workshop on Information Hiding. 2000:61-76

3. Provos N., Honeyman P. Detecting steganographic content on the internet. Technical Report CITI 01-1a, University of Michigan, 2001.

4. Aliev A.T. On application LSB steganographics method to digital images with the greater monochrome areas. Vestnik DGTU. 2004;4(22):454-460.

5. Barsukov V.S., Romancov A.P. Assessment of the stealth level of multimedia steganographic channels for storing and transmitting information. Specialnaya Tekhnika. 2000:1.

6. Kustov V.N., Paraskevopulo A.Ju.. Simple secrets of steganalysis. Zashhita informacii, INSIDE. 2005;4:72-78.

7. Golub V.A., Drjuchenko M.A. Comprehensive approach for revealing steganographic concealment in JPEG files Comprehensive approach for revealing steganographic concealment in JPEG files. Infokommunikacionnye tehnologii. 2009;7(1):44-50.

8. Abdenov A.Zh., Leonov L.S. The use of neural networks in blind methods for detecting embedded steganographic information in digital images. Polzunovskij Vestnik. 2010;2:221-225.

9. Zhilkin M.Ju. Stegoanalysis of graphic data in various formats. Doklady TUSURa. 2008;2(18):63-64.

10. Monarev V. A. Shift detection of hidden information. Vestnik SibGUTI. 2012;4:62-68.

11. Abreu E., Lightstone M., Mitra S.K., Arakawa S.K. A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Transactions on Image Processing. 1996;5:1012-1025.

12. Garnett R., Huegerich T., Chui C., He W. A Universal Noise Removal Algorithm with an Impulse Detector. IEEE Trans Image Proccess, 2005;14(11):1747-1754.

13. Sorokin S.V., Shherbakov M.A. SD-ROM filter implementation based on fuzzy logic concept. Izvestija vysshih uchebnyh zavedenij. Povolzhskij region. 2007;3:56-65

14. Amritha P.P., Sreedivya Muraleedharan M., Rajeev K. and Sethumadhavan M. Steganalysis of LSB Using Energy Function. Advances in Intelligent Systems and Computing. 2016;384:549-558.

15. Debasis Mazumdar, Apurba Das, and Sankar K. Pal MRF Based LSB Steganalysis: A New Measure of Steganography Capacity. S. Chaudhury et al. (Eds.): PReMI 2009, LNCS 5909. 2009:420–425.

16. Yun Q. Shi, Patchara Sutthiwan, and Licong Chen Textural Features for Steganalysis. M. Kirchner and D. Ghosal (Eds.): IH 2012, LNCS 7692. 2013:63–77.

17. Ojala, T., Pietikainen, M., Maenpaa, T. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24. 2002:971–987.

18. Ojala, T., Pietikainen, M., Harwood, D. A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29. 1996:51–59.

19. Saaty T.L. Relative Measurement and its Generalization in Decision Making: Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors - The Analytic Hierarchy/Network Process. Review of the Royal Spanish Academy of Sciences, Series A, Mathematics. 2008;102(2):251–318.

Guts Alexander Konstantinovich
Doctor of Physical and Mathematical Sciences Professor
Email: aguts@mail.ru

Omsk State University. F.M. Dostoevsky (OmSU)

Omsk, Russian Federation

Vilkhovskiy Danil Eduardovich

Email: vilkhovskiy@gmail.com

Omsk State University. F.M. Dostoevsky (OmSU)

Omsk, Russian Federation

Keywords: identification of stego inserts, analysis of images with inserts, analysis of stegocontainer, search for lsb inserts, embedding lsb

For citation: Guts A.K. Vilkhovskiy D.E. An algorithm for identifying steganographic inserts of the LSB-replacement type based on the hierarchy analysis. Modeling, Optimization and Information Technology. 2020;8(2). Available from: https://moit.vivt.ru/wp-content/uploads/2020/05/GutsVilkhovskiy_2_20_1.pdf DOI: 10.26102/2310-6018/2020.29.2.006 (In Russ).


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Published 26.07.2021