STUDY OF THE PARAMETERS OF THE DIGITAL CONTENT MATRIX BLOCKS IN DIFFERENT STORAGE FORMATS AS A THEORETICAL BASIS FOR THE METHODS OF DETECTING VIOLATIONS OF ITS INTEGRITY
DOI:
https://doi.org/10.17721/2519-481X/2023/78-11Keywords:
digital image, digital video, lossy format, lossless format, singular valueAbstract
Unauthorized changes of digital information contents, in particular images, videos, which are considered in the work, the detection of which is a difficult and urgent task, require the development of new approaches and methods. In case of unauthorized changes in digital contents, there is often a change in the format (lossy/lossless) of its preservation (in whole or in part), in particular when organizing a steganographic communication channel, photomontage, etc. Thus, the identification of the fact of re-preservation of digital content in a format different from the original one is a pointer to the violation of its integrity, making the task of separating content in different formats urgent. The aim of the work is to study the properties of the formal parameters of blocks of original digital content to create a theoretical basis for the methods of separating content in various storage formats. In the course of the study: the formal parameters – the smallest singular values of the blocks of the corresponding matrices, based on the properties of which the proposal to introduce a formal research object – the matrix of the smallest singular values of the blocks, corresponding to the digital content and having properties that differ depending on from the digital content storage format – were determined; for a sequence of digital images of the same format, for digital video, a formal mathematical object is defined – a histogram of modes of histograms of matrices of the smallest singular values of blocks of images/frames of video, the properties of which differ significantly for different storage formats, which can be used to develop an appropriate expert method. Establishing quantitative characteristics for qualitative separators obtained in the work will provide an opportunity to form effective methods of separating digital contents in various storage formats, which can be applied as a component of the steganalysis process, in the process of detecting the results of photomontage, where contents in various formats were involved, etc.
References
1. Rai, A., Singh, A.S., Kumar, A.S. (2020), “A review of information security: issues and techniques”, International Journal for Research in Applied Science & Engineering Technology, 8(5), pp. 953–960.
2. Shwetha, B., Sathyanarayana, S.V. (2017), “Digital image forgery detection techniques: a survey”, ACCENTS Transactions on Information Security, 2(5), pp. 22–31.
3. Alqahtani, F.H. (2017), “Developing an information security policy: a case study approach”, Procedia Computer Science, 124, pp. 691–697.
4. Karthikeyan, N., Saravana Kumar, N.M., Mugunthan, S.R. (2018), “Comparative study of lossy and lossless image compression techniques”, International Journal of Engineering & Technology, 7, pp. 950–953.
5. Gonzalez, R.C., Woods, R.E. (2006), “Tsifrovaya obrabotka izobrazheniy” [Digital Image Processing], Technosfera, Moscow, 1070 p.
6. Taher, M.M., Ahmad, A.R., Hameed, R.S., Mokri, S.S. (2022), “А literature review of various steganography methods”, Journal of Theoretical and Applied Information Technology, 100(5), pp. 1412–1427.
7. Aggarwal, A., Sangal, A., Varshney, A. (2019), “Image steganography using LSB algorithm”, International Journal of Information Sciences and Application, 11(1), pp. 85–89.
8.Dhawan, S., Gupta, R. (2021), “Analysis of various data security techniques of steganography: a survey”, Information Security Journal: A Global Perspective, 30(2), pp. 63–87.
9. Kobozeva, A.A. (2008), “Ispol’zovanie osobennostey vozmuscheniy singulyarnyh chisel matritsi tsifrovogo izobrazheniya dlya obnaruzheniya ego fal’sifikatsii” [Application of image matrix singular values disturbances for image forgery detection], Artificial Intelligence, 1, pp. 145–153.
10. Jalab, H.A., Subramaniam, T., Ibrahim, R.W., Kahtan, H., Mohd Noor, N.F. (2019), “New texture descriptor based on modified fractional entropy for digital image splicing forgery detection”, Entropy, 21(4), 371.
11. Tjoa, S., Lin, W.S., Zhao, H.V., Liu, K.J.R. (2007), “Block size forensic analysis in digital images”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2007, April 15-20, 2007, Honolulu, Hawaii, USA. pp. I-633-I-636.
12. Luo, W., Huang, J., Qiu, G. (2008), “A novel method for block size forensics based on morphological operations”, Digital Watermarking (IWDW 2008), Lecture Notes in Computer Science, 5450, pp. 229–239.
13. 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.
14. Akhmametieva A.V. (2016), “Method of detection the fact of compression in digital images as an integral part of steganalysis”, Informatics and mathematical methods in modelling, 6(4), pp. 357–364.
15. Bobok, I.I. (2017), “Metod vyyavlennyazobrazhen’, perezberezhennyh u format bez vtrat z formatu z vtratamy” [A method for detecting images converted to a lossless format from a lossy format], Mathematical and Computer Modelling. Series: Technical Sciences, 16, pp. 5–14.
16. Kobozeva, A.A., Khoroshko, V.A. (2009), “Analiz informatsionnoy bezopasnosti” [Information Security Analysis], GUIKT, Kyiv, 251 p.
17. 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.
18. 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.
19. Demmel, D. (2001), “Vychislitelnaya linejnaya algebra: teoriya i prilozheniya” [Numerical Linear Algebra: Theory and Applications], Mir, Moscow, 430 p.
20. Kahaner, D., Moler, C., Nash, S. (2001), “Chislennie metody i programmnoe obespechenie” [Numerical Methods and Software], Mir, Moscow, 573 p.
21. Gloe, T., Böhme, R. (2010), “The “Dresden Image Database” for benchmarking digital image forensics”, Proceedings of the 2010 ACM Symposium on Applied Computing (SAC ’10), pp. 1585–1591.






