METHOD OF DETECTING VIOLATION OF DIGITAL IMAGE INTEGRITY BASED ON SPECTRAL DECOMPOSITION OF SYMMETRIZED BLOCK MATRIX
DOI:
https://doi.org/10.17721/2519-481X/2022/77-08Keywords:
digital image, integrity violation, eigenvector, eigenvalueAbstract
The work considers an important scientific and practical task of increasing the effectiveness of detecting violations of the integrity of information, in particular digital images, which is its common representation, which is becoming one of the main ones for specialists in the field of information and cyber security today. Undetected, unauthorized changes to information in a timely manner can lead to negative, catastrophic consequences for individuals, enterprises, banks, firms, and for humanity as a whole, when it comes to information that constitutes a state secret, contains data from the military industry, nuclear energy, chemical industry, etc., which determines the relevance of the problem under consideration. The main result
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of the work is an improved universal method for detecting violations of the integrity of a digital image, ready for practical implementation, the theoretical basis of which is based on the analysis of eigenvalues and eigenvectors of symmetric blocks of the image matrix, which correspond to the original blocks. The paper substantiates the method of symmetrization of the block matrix, which allows to significantly (by more than 23%) reduce computational and, as a result, time costs for image examination in comparison with the time costs of the prototype method. It is proved that for the majority of the obtained symmetric blocks that correspond to the blocks of the original CG, the angle between the eigenvector corresponding to the maximum eigenvalue of the block and the normalized vector of the modules of the eigenvalues is equal to a certain value that does not depend on the specifics of the original image, but is sensitive to its changes, which made it possible to ensure the universality of the method and increase its efficiency in the sense of the accuracy of detecting a violation of the integrity of the image by more than 5%, compared to the analogue. The significance of the obtained results lies in ensuring, due to the use of the proposed method, an increase in the efficiency of the process of detecting violations of the integrity of the image according to the criteria of computing (time) costs for the examination of one image and the accuracy of detection.
References
1. Pirtskhalava, L.G., Khoroshko, V.A., Khokhlacheva, J.E., Shelest, M.E. (2019), “Informatsionnoe protivoborstvo v sovremennyh usloviyah” [Information Warfare in Modern Conditions], Komprint, Kyiv, 226 p.
2. Uliyan, D.M., Jalab, H.A., Abdul Wahab, A.W., Sadeghi, S. (2016), “Image region duplication forgery detection based on angular radial partitioning and Harris key-points”, Symmetry, 8(7), 62.
3. Zadiraka, V.K. (2014), “Suchasni metody rozvyazannya zadach informatsiynoy bezbeky” [Modern Methods for Solving the Tasks of Information Safety], Visnyk of the National Academy of Sciences of Ukraine, 5, pp. 65–69.
4. Mandal, P.C., Mukherjee, I., Paul, G., Chatterji, B.N. (2022), “Digital image steganography: A literature survey”, Information Sciences, 609. pp. 1451–1488.
5. Borysenko, I.I. (2020), “Vyyavlennya tsyfrovogo fotomontazhu na osnovi kntrstnosti zobrazhennya” [Detection of digital photomontage based on image contrast analysis], Modern Information Security, 2, pp. 47–51.
6. Joglekar, N.P., Chatur, P.N. (2015), “A compressive survey on active and passive methods for image forgery detection”, International Journal of Engineering and Computer Science, 4(1), pp. 10187–10190.
7. Shwetha, B., Sathyanarayana, S.V. (2017), “Digital image forgery detection techniques: a survey”, ACCENTS Transactions on Information Security, 2(5), pp. 22–31.
8. Thakur, T., Singh, K., Yadav, A. (2018), “Blind approach for digital image forgery detection”, International Journal of Computer Applications, 179(10), pp. 34–42.
9. Chu, X., Li, H. (2019), “A Survey of Blind Forensics Techniques for JPEG Image Tampering”, Journal of Computer and Communications, 7(10), pp. 1–13.
10. Bobok, I.I. (2017), “Rozvytok zagalnogo pidhodu do problem vyyavlennya porushen’ tsilisnosti tsyfrovyh zobrazhen” [Development of a general approach to the problem of detecting integrity violations of digital images] / Legal, Regulatory and Metrological Support of Information Security System in Ukraine, 2, pp. 78–88.
11. Kobozeva, A.A., Bobok, I.I., Garbuz, A.I. (2016), “General principles of integrity checking of digital images and application for steganalysis”, Transport and Telecommunication Journal, 17(2), pp. 128–137.
12. Lerch-Hostalot, D., Megias, D. (2016), “Unsupervised steganalysis based on artificial training sets”, Engineering Applications of Artificial Intelligence, 50, pp. 45–59.
13. Bobok, I.I. (2018), “Steganalysis method for detection of the hidden communication channel with low capacity”, Telecommunications and Radio Engineering, 77(18), pp. 1597–1604.
14. Lebedieva, E.J., Kobozieva, A.A. (2013), “Osnovy metoda vyyavleniya klonirovannyh uchastkov izobrazheniy, podvergnutyh korrektsii yarkosti” [Fundamentals of the method for detecting cloned image areas subjected to brightness correction], Modern Special Technics, 3, pp. 17–24.
15. Li, H., Luo, W., Qiu, X., Huang, J. (2017), “Image forgery localization via integrating tampering possibility maps”, IEEE Transactions on Information Forensics and Security, 12(5), pp. 1240–1252.
16. Tryfonova, K.O. (2017), “Metod vyyavlennya porushennya tsilisnosti tsyfrovogo zobrazhennya shumom Perlina” [A method of detecting a violation of the integrity of a digital image by Perlin noise], Radio Electronics, Computer Science, Control, 2, pp. 134–142.
17. Khan, S., Khan, K., Ali, F., Kwak, K.-S. (2020), “Forgery detection and localization of modifications at the pixel level”, Symmetry, 12(1), 137.
18. Al-Jarrah, M.M, Al-Taie, Z.H., Abuarqoub, A. (2017), “Steganalysis using LSB-focused statistical features”, Proceedings of the International Conference on Future Networks and Distributed Systems (ICFNDS’17), article 54, pp. 1–5.
19. Zorilo, V.V., Kioseva, O.I., Zorilo, I.V. (2018), “Modyfikatsiya algorytmu vyyavlennya shtuchnogo pidvyschennya rizkosti tsyfrovogo zobrazhennya” [Modification of algorithm for detecting artificial improvement of sharpness of the digital image], Informatics and Mathematical Methods in Simulation, 2, pp. 156–163.
20. Duan, X.T., Peng, T., Li, F.F., Wang, J. (2017), “Blind separation of tampered images based on JPEG double compression properties”, Journal of University of Jinan (Science and Technology), 31, pp. 87–96.
21. Bobok, I.I., Kobozeva, A.A. (2019), “Method for detecting of digital image integrity violations due to its block processing”, Radiotechnika, 199, pp. 130–141.
22. Geetha, S., Sindhu, S., Kamaraj, N. (2009), “Close color pair signature ensemble adaptive threshold based steganalysis for LSB embedding in digital images”, Transactions on Data Privacy, 1, pp. 140–161.
23. Gonzalez, R.C., Woods, R.E. (2006), “Tsifrovaya obrabotka izobrazheniy” [Digital Image Processing], Technosfera, Moscow, 1070 p.
24. Bergman, C., Davidson, J. (2005), “Unitary embedding for data hiding with the SVD”, Security, steganography and watermarking of multimedia contents VII, SPIE, 5681, pp. 619–630.
25. Demmel, D. (2001), “Vychislitelnaya linejnaya algebra: teoriya i prilozheniya” [Numerical Linear Algebra: Theory and Applications], Mir, Moscow, 430 p.
26. Kobozeva, A.A. (2014), “Osnovy obschego podhoda k razrabotke universalnyh steganoanaliticheskih metodov dlya tsifrovyh izobrazheniy” [A basis of common approach to the development of universal steganalysis methods for digital images], Odes’kyi Politechnichnyi Universytet. Pratsi, 2, pp 136–146.
27. Gantmacher, F.R. (2004), “Teoriya matrits: monografiya” [Matrix Theory], FizMatLit, Moscow, 559 p.
28. Srinivas, R., Panda, S. (2013), “Performance analysis of various filters for image noise removal in different noise environment”, International Journal of Advanced Computer Research, 3, pp. 47–52.
