Cybernetics And Systems Analysis logo
Editorial Board Announcements Abstracts Authors Archive
Cybernetics And Systems Analysis
International Theoretical Science Journal
-->

UDC 621.317+681.849
V.I. Solovyov1, O.V. Rybalskiy2, V.V. Zhuravel3,
O.M. Shablya4, Ye.V. Tymko5



1 Silentium Systems, Vancouver, Canada

edemsvi@gmail.com

2 National Academy of Internal Affairs, Kyiv, Ukraine

rov_1946@ukr.net

3 Kyiv Scientific-Research Expert-Forensic Center
of the Ministry of the Interior Affairs of Ukraine,
Kyiv, Ukraine

fonoscopia@ukr.net

4 Odesa Scientific-Research Institute of Forensic
Expertise of the Ministry of Justice of Ukraine,
Odesa, Ukraine

alik_shablya@gmail.com

5 Kyiv Scientific-Research Institute of Forensic
Expertise of the Ministry of Justice of Ukraine,
Kyiv, Ukraine

e.tymko@kndise.gov.ua

METHOD FOR IDENTIFICATION OF DIGITAL VIDEO RECORDING EQUIPMENT
AND DIGITAL CAMERAS

Abstract. A method for constructing expert tools for forensic identification of digital video recording equipment and digital cameras is considered. The necessity of creating such tools is substantiated. It is proposed to carry out the identification of this equipment by the statistical characteristics of its own noise, extracted from digital images recorded by such equipment. The features and main sources of such noise in digital images are shown. For its selection and processing, wavelet analysis based on the Haar wavelet is used. The final result of the examination is obtained by applying deep learning neural networks. The obtained results of the created identification system for such equipment showed its high efficiency.

Keywords: digital video recording equipment, digital photography equipment, wavelet analysis, Haar wavelet, forensic identification, deep learning neural networks, intrinsic noise, digital images, forensics.


FULL TEXT

REFERENCES

  1. Register of forensic examination methods of the Ministry of Justice of Ukraine. URL: http://rmpse.minjust.gov.ua/44 (request date: 03.02.2022).

  2. Solovyov V.I., Rybalskiy O.V., Zhuravel V.V., Semenova N.V. Analyzing the models of speech recognition on the basis of neural networks of deep learning for examination of digital phonograms. Cybernetics and Systems Analysis. 2021. Vol. 57, N 1. P. 133–138. https://doi.org/10.1007/s10559-021-00336-y .

  3. Solovyov V.I., Rybalskiy O.V., Zhuravel V.V. Method of exposure of signs of the digital editing in phonograms with the use of neuron networks of the deep learning. Journal of Automation and Information Sciences. 2020. Vol. 52, Iss. 1. P. 22–28. doi.org//10.1615/JAutomatInfScien.v52.i1.30/

  4. Solovyov V.I., Rybalskiy O.V., Zhuravel V.V. Substantiating the fundamental fitness of deep learning neural networks for construction of a phonogram digital processing detection system. Cybernetics and Systems Analysis. 2020. Vol. 56, N 2. P. 326–330. https://doi.org//10.1007/s10559-020-00249-2.

  5. Rybalskiy O.V., Solovyov V.I., Zhuravel V.V., Shablya A.N., Tymko E.V. Expert tools for detecting traces of processing in digital phonograms based on deep learning neural networks. Suchasna spetsialʹna tekhnika. 2021. N 1. P. 101–111. https://doi.org/10.36486/mst2411–3816.2021.1(64).

  6. Solovyov V.I., Rybalskiy O.V., Zhuravel V.V., Shablya A.N., Tymko E.V. Information redundancy in constructing systems for audio signal examination on deep learning neural networks. Cybernetics and Systems Analysis. 2022. Vol. 58, N 1. P. 8–15. https://doi.org/ 10.1007/s10559-022-00429-2 .

  7. Solovyov V., Rybalskiy O., Zhuravel V., Shablya A., Tymko Ye. Building of a speaker’s identification system based on deep learning neural networks. Proc. International Scientific and Practical Conference "Intelligent Systems and Information Technologies" (September 13–19, 2021, Odesa, Ukraine). Odesa, 2021. P. 107–115.

  8. Kobozeva A.A., Bobok I.I., Garbuz A.I. General principles of integrity checking of digital images and application for steganalysis. Transport and Telecommunication. 2016. Vol. 17, N 2. Р. 128–137. URL: http://journal.ie.asm.md/ru/contents .

  9. Rybalskiy O.V., Solovyov V.I. A model for the manifestation and detection of the influence of the nonmonotonicity of the static characteristic of the level quantizer in the output signal of the analog-to-digital-to-analog conversion system. Informatyka ta matematychni metody v modelyuvanni. 2014. Vol. 4, N 4. P. 337–341.

  10. Mallat S. A wevlet tour of signal processing. New Yогk: Асаdemic Ргеss, 1999. 670 p.




© 2022 Kibernetika.org. All rights reserved.