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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Druckformat: 0040-2508
ISSN Online: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i8.30
pages 671-681


A. V. Akhmametieva
Odessa National Polytechnic University, 1 Shevchenko Ave, Odessa 65044, Ukraine
M. C. Bwabwa
Odessa National Polytechnic University, 1 Shevchenko Ave, Odessa 65044, Ukraine


An improvement of the steganalytic method for detection of the presence of additional information in color digital images which showed high efficiency in identifying stegano formed by embedding of secret data only into one color component of the container is presented. The proposed method analyses digital image in the spatial domain and is based on the accounting of sequential color triads in the matrix of unique colors of the digital content. However, in the process of steganographic transformation cases of embedding of confidential data into two and three color components of images are possible that ensures the concealment of a larger amount of information and requires the improvement of the existing method of steganalysis. In the course of the conducted research the character of perturbations in the quantity of sequential triads of triplets in a matrix of unique colors as a result of embedding of additional information into two and three color components of images originally stored in a losses format was analyzed. Considering obtained results the parameters of the original method for detecting of stegano was refined. It has been established that the character of changes in the quantity of sequential triads of triplets as a result of steganographic transformation is different in cases of using containers in a losses format and containers in a lossless format. Based on the obtained data the steganalytic method has been improved by integrating it with the method of detection the fact of compression of digital content developed earlier. The developed method provides high efficiency in detecting stego formed with different degree of container fullness without reducing the accuracy of identifying the filled color components if the additional information was embedded into only one color component of the digital images. This method can be used as a basis for complex steganalysis of digital contents by using existing methods that analyzes color matrixes of images separately.


  1. Agranovskiy, A.V., Balakin, A.V., Gribunin, V.G., and Sapozhnikov, S.A., (2009) , Steganography, Digital Water Symbols and Steganalysis, Moscow, Russia: Vuz. Kniga, 220 p., (in Russian).

  2. Bohme, R., (2010) , Advanced Statistical Steganalysis, Springer, 302 p.

  3. Couchot, J.-F., Couturier, R., and Salomon M., (2017), Improving Blind Steganalysis in Spatial Domain using a Criterion to Choose the Appropriate Steganalyzer between CNN and SRM+EC, ICT Systems Security and Privacy Protection, 32nd IFIP TC 11 International Conference, SEC 2017, Rome, Italy, pp. 327-340. DOI: https://doi.org/10.1007/978-3-319-58469-0_22

  4. Wei Huang and Xianfeng Zhao, (2016) , Novel cover selection criterion for spatial steganography using linear pixel prediction error, Science China. Information Sciences, 59, pp. 059103:1–059103:3. DOI: 10.1007/s11432-016-5530-z

  5. Denemark T., Fridrich, J., and Comesaña-Alfaro, P., (2016) , Improving Selection-Channel-Aware Steganalysis Features, IS&T International Symposium on Electronic Imaging, pp. MWSF-080.1- MWSF-080.8.

  6. NRCS Photo Gallery: United States Department of Agriculture. Washington, USA: http://photogallery.nrcs.usda.gov.

  7. Uncompressed Color Image Database (UCID) Multimedia Phylogeny Datasets: http://www.recod.ic.unicamp.br/~oikawa/datasets.html.

  8. McGill Calibrated Color Image Database Fred Kingdom's Laboratory at McGill Vision Research: http://tabby.vision.mcgill.ca/html/welcome.html.

  9. Never-compressed image database, Sam Houston State University: http://www.shsu.edu/qxl005/New/Downloads/.

  10. Akhmametieva, A., (2016) , Steganalysis of digital images, kipping in the losses format, Zakhyst Informaz., 23, pp.135-145, (in Russian).

  11. Akhmametieva, A., (2017) , Steganalysis of digital contents, based on the analysis of unique color triplets, Annales Mathematicae et Informaticae, 47, pp. 3-18.

  12. Akhmametieva, A., (2016) , Method of detection the fact of compression in digital images as an integral part of steganalysis, Informatika i Mathemat. Metody v Modelyuvanni, 6(4), pp. 357-364, (in Russian).