Abstract
The paper deals with a research aimed at providing the highest performance of detection of an embedding into a wavelet-domain image, in particular in LH and HL subbands, due to the combined use of several methods proposed by the authors. The paper discusses various methods for enhancing detection of the embedding into a wavelet-domain image as proposed by the authors for their possible combined use in order to ensure the best possible performance of detecting the embedding into a wavelet-domain image. These methods use the features of the wavelet’s transform, interrelations of various domains of the coefficients obtained by the wavelet transform of the image, or peculiar features or frequency domain of images. By the results of this research, a steganalysis method is proposed, based on the combined use of the above-described methods for increasing the steganalysis efficiency, which allows providing a better performance of detection of a wavelet-domain image embedding compared with existing methods of the steganalysis. The proposed methods of increasing the efficiency of steganalysis will improve the effectiveness of the steganalysis of information embedded in the LH and HL subbands by 4–7% in comparison with the already existing methods. The proposed method is based on combining them and allows you to get an additional increase in efficiency by 1–3%. The results of the given research can be useful for the experts, dealing with the information security problems while detecting and counteracting with the hidden data channel, based on the use of steganography, including in the mobile Internet. The obtained results can be used in the development of steganalysis systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
FBI: Spies hid secret messages on public websites. https://www.wired.com/2010/06/alleged-spies-hid-secret-messages-on-public-websites/
Security foresignt: Steganography: BISA: Business information security association. http://bis-expert.ru/blog/660/56301
Evsyutin, O., Negacheva, E.: Steganographic embedding of information into digital images, compressed using block cellular automaton. Reports of Tomsk State University of Control Systems and Radioelectronics, no. 4(30), pp. 130–135 (2013)
Vyas, A., Dudul, S.: Study of image steganalysis techniques. Int. J. Adv. Res. Comput. Sci. 6(8), 7–11 (2015)
Kotenko, I., Saenko, I., Kushnerevich, A.: Parallel big data processing system for security monitoring in Internet of Things networks. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. (JoWUA) 8(4), 60–74 (2017)
Desnitsky, V., Levshun, D., Chechulin, A., Kotenko, I.: Design technique for secure embedded devices: application for creation of integrated cyber-physical security system. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. (JoWUA) 7(2), 60–80 (2016)
Sivachev, A., Prokhozhev, N., Mikhaylichenko, O.: Improving of steganalysis accuracy by optimizing parameters of wavelet transformation methods. Sci. Tech. Newsl. Inf. Technol. Mech. Optics 1, 113–121 (2018)
Kolomeec, M., Chechulin, A., Pronoza, A., Kotenko, I.: Technique of data visualization: example of network topology display for security monitoring. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. (JoWUA) 7(1), 58–78 (2016)
Hashem, Y., Takabi, H., GhasemiGol, M., Dantu, R.: Inside the mind of the insider: towards insider threat detection using psychophysiological signals. J. Internet Serv. Inf. Secur. (JISIS) 6(1), 20–36 (2016)
Kumar, G., Jithin, R., Deepa, D.: Shankar feature based steganalysis using wavelet decomposition and magnitude statistics. In: Advances in Computer Engineering (ACE), pp. 298–300 (2010)
Farid, H.: Detecting Steganographic Messages in Digital Images. Technical report TR2001-412, Dartmouth College, Computer Science Department (2001)
Liu, C., Ouyang, C., Guo, M., Chen, H.: Image steganalysis based on spatial domain and DWT domain features. In: Proceedings of the 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 01, pp. 329–331 (2010)
Shi, Y., et al.: Effective steganalysis based on statistical moments of wavelet characteristic function. In: IEEE Conference on Information Technology: Coding and Computation (ITCC05), Las Vegas, Nevada, USA (2005)
Sivachev, A., Prokhozhev, N., Mikhailichenko, O., Bashmakov, D.: Efficiency of steganalysis based on machine learning methods. Sci. Tech. Newsl. Inf. Technol. Mech. Opt. 17(3), 457–466 (2017)
Sivachev, A., Mikhaylichenko, O., Prokhozhev, N., Bashmakov, D.: Improved accuracy in DWT steganalysis by using the correlation between domains of bi-dimensional and single-dimensional decompositions. Cybern. Program. 2, 78–87 (2017)
Sivachev, A.: Increase of steganalysis efficiency of DWT image domain by analyzing the image domain frequency parameters. Cybern. Program. 2, 29–37 (2018)
Acknowledgements
This work was supported by Government of Russian Federation (Grant 08-08).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sivachev, A.V., Bashmakov, D.A., Mikhailishenko, O.V., Korobeynikov, A.G., Rieke, R. (2019). Steganalysis Method for Detecting Embedded Coefficients of Discrete-Wavelet Image Transformation into High-Frequency Domains. In: You, I., Chen, HC., Sharma, V., Kotenko, I. (eds) Mobile Internet Security. MobiSec 2017. Communications in Computer and Information Science, vol 971. Springer, Singapore. https://doi.org/10.1007/978-981-13-3732-1_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-3732-1_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3731-4
Online ISBN: 978-981-13-3732-1
eBook Packages: Computer ScienceComputer Science (R0)