Spectral Characteristics Analysis of Images Matrix Masking Results
https://doi.org/10.31854/1813-324X-2024-10-2-76-82
EDN: TUWNCW
Abstract
The article describes the results of a computational experiment to assess the capabilities of extracting useful information if an image masked by quasi-orthogonal matrices sent over an open channel became available to a third party. Hadamard and Mersenne matrices of symmetric and cyclic structure are considered. The results confirm the data that images masked by small-sized matrix leaves edges of the original image on the masked image. However, with an increase in the size of the masking matrix, all considered in the article matrices reliably hides the original image during visual analysis. Masking by symmetric Mersenne-Walsh matrices and cyclic Mersenne matrices based on modified M-sequences provides better spectral secrecy of masked images in comparison with Hadamard matrices. Mersenne matrices of cyclic structure, with equal sizes of the image and the masking matrix, bring the phase spectrum of the masked image to a form close in spectrum to uniform noise, which makes their use more preferable based on the considerations that the human visual system is extremely sensitive to phase-frequency distortions of the visual information.
Supporting agencies: The work was supported by the Ministry of Science and Higher Education of the Russian Federation, agreement No. FSRF-2023-0003
About the Author
E. Grigoriev
Saint-Petersburg State University of Aerospace Instrumentation
Russian Federation
Russian Federation
References
1. Balonin N., Sergeev M. Local Maximum Determinant Matrices. Information and control systems. 2014;1(68):2−15. (in Russ.) EDN:RYEXEH
2. Vostrikov A., Sergeev M., Litvinov A. Masking of Digital Visual Data: the Term and Basic Definitions. Information and control systems. 2015;5(78):116−123. (in Russ.) DOI:10.15217/issn1684-8853.2015.5.116. EDN:UQFATJ
3. Grigoriev E., Sergeev A. Quality Analysis of Matrix Masking of Digital Audio Data. Proceedings of Telecommunication Universities. 2023;9(3):6−13. (in Russ.) DOI:10.31854/1813-324X-2023-9-3-6-13. EDN:AJFFXQ
4. Grishancev А., Korobeinikov A. Search algorithm, some properties and application of matrices with complex element values for steganography and synthesis of broadband signals. Journal of Radio Electronics. 2016;5:9. (in Russ.) EDN:WNDAOR
5. Frolov A., Chobal A., Rizak V. Encryption of color images using Hadamard matrices. Ukrainian information security research journal. 2019;21(4):241−246. (in Russ.) DOI:10.18372/2410-7840.21.14312. EDN:WHUZFD
6. Yuan X., Zhang L., Chen J., Zhang D. Multiple-image encryption scheme based on ghost imaging of Hadamard matrix and spatial multiplexing. Applied Physics B. 2019;125:174. DOI:10.1007/s00340-019-7286-9
7. Sergeev А. Two-level and modularly two-level quasi-orthogonal Walsh-structured matrices for image masking. Journal of Instrument Engineering. 2023;66(5):399−408. (in Russ.). DOI:10.17586/0021-3454-2023-66-5-399-408. EDN:SVIYSL
8. Sergeev A. Interrelation of Symmetry and Antisymmetry of Quasi-Orthogonal Cyclic Matrices with Prime Numbers. Proceedings of the Telecommun. Univ. 2022;8(4):14−19. (in Russ.) DOI:10.31854/1813-324X-2022-8-4-14-19. EDN:PXAZIG
9. Vostrikov A., Mishura O., Sergeev A., Chernyshev S. The choice of matrices for images masking and demasking procedures. Fundamental research. 2015;2(24):5335−5339. (in Russ.) EDN:UADDQR
10. Vostrikov А., Chernyshev S. On assessing the resistance to distortion of images masked by M-matrices. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2013;5(87):99−103. (in Russ.) EDN:RBXRDX
11. Balonin N., Sergeev M. Odin and Shadow Cretan matrices accompanying primes and their powers. Information and control systems. 2022;1(116):2−7. (in Russ.) DOI:10.31799/1684-8853-2022-1-2-7. EDN:LTLVEM
12. Grigoriev E., Nenashev V., Sergeev A., Samohina E. Search and Modification of Code Sequences Based on Persymmetric Quasi-Orthogonal Circulants. Telecommunications. 2020;10:27‒33. (in Russ.) EDN:EGQMAS
13. Chekotilo E., Kuznetsov P. Spectral analysis of probabilistic characteristics of images. Journal of Samara state technical university, ser. Physical and mathematical sciences. 2006;42:212‒215. (in Russ.) EDN:IPKLTB
14. Gonzalez R., Woods R. Digital Image Processing. New York: Pearson Publ.; 2017. 1192 p.
15. The Lenna Story. URL: http://lenna.org [Accessed 19.03.2024]
16. Erosh I., Sergeev A., Filatov G. Protection of Images During Transfer Via Communication Channels. Information and control systems. 2007;5(30):20−22. (in Russ.). EDN:ITVYKT
17. Кrasilnikov N.N. Theory of image transmission and perception. Moscow: Radio i sviaz Publ.; 1987. 656 p. (in Russ.)
Review
For citations:
Grigoriev E. Spectral Characteristics Analysis of Images Matrix Masking Results. Proceedings of Telecommunication Universities. 2024;10(2):76-82. (In Russ.) https://doi.org/10.31854/1813-324X-2024-10-2-76-82. EDN: TUWNCW
Views:
232
Email this article 