Skip to main content
SpringerLink
Log in

Improving the reliability of digital camera identification by optimizing the algorithm for comparing noise signatures

  • Published:
Measurement Techniques Aims and scope

Abstract

In this paper, the issues on current optimization methods for identifying modern digital camera photosensors are presented. Methods for improving the reliability of identification of digital cameras are discussed, i.e., determining the digital camera used to take a particular photograph. Homogeneous images were optically recorded to form a noise signature, and sets of amateur images were tested for identification of three types of cameras from the images. Digital image filtering and image identity metric were optimized. An optimal digital filter was selected to evaluate smoothed images to obtain the noise signatures of the identified cameras. An optimal identity criterion was obtained by comparison of camera noise signatures. Using an optimal filter and identity criterion allowed increasing, on average, the reliability of identifying the camera used to take a particular photograph by > 60 times. This work provides reference to solve image problems in image registration and processing, security, forensics, and big data analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Fan, Y., Li, J., Guo, Y., Xie, L., Zhang, G.: Measurement 171, 108829 (2021). https://doi.org/10.1016/j.measurement.2020.108829

    Article  Google Scholar 

  2. van Kesteren, J., van Goudoever, L.A.E., Conteh, A., van Acker, G.J.D., Bonjer, H.J., Bolkan, H.A.: J Surg.Educ 80(4), 495–498 (2023). https://doi.org/10.1016/jJsurg.2023.01.003

    Article  Google Scholar 

  3. Lane, B.A., Cardoza, R.J., Lessner, S.M., Vyavahare, N.R., Sutton, M.A., Eberth, J.F.: J Mech Behav Biomed Mater 141, 105745 (2023). https://doi.org/10.1016/jJmbbm.2023.105745

    Article  Google Scholar 

  4. Putro, M.D., Nguyen, D.-L., Jo, K.-H.: IEEE Sens J 22(1), 565–574 (2021). https://doi.org/10.1109/jsen.2021.3128389

    Article  ADS  Google Scholar 

  5. Emilsson, M., Karlsson, C., Svensson, A.: BMC Health Serv Res 23(1), 144 (2023). https://doi.org/10.1186/s12913-023-09130-2

    Article  Google Scholar 

  6. Roshchin, D.A.: Meas Tech 64(2), 100–108 (2021). https://doi.org/10.1007/s11018-021-01903-7

    Article  Google Scholar 

  7. Baguenard, B., Bensalah-Ledoux, A., Guy, L.: Nat Commun 14, 1065 (2023). https://doi.org/10.1038/s41467-023-36782-9

    Article  ADS  Google Scholar 

  8. Samoilenko, M.V.: Meas Tech 66(5), 311–319 (2023). https://doi.org/10.1007/s11018-023-02229-2

    Article  Google Scholar 

  9. Roshchin, D.A.: Meas Tech 65(1), 24–32 (2022). https://doi.org/10.1007/s11018-022-02045-0

  10. Roshchin, D.A.: Meas Tech 65(3), 180–187 (2022). https://doi.org/10.1007/s11018-022-02066-9

    Article  Google Scholar 

  11. Yu, H., Kim, Y., Yang, D.: Nat Commun 14, 3534 (2023). https://doi.org/10.1038/s41467-023-39329-0

    Article  ADS  Google Scholar 

  12. Milanfar, P.: IEEE Signal Process. Mag. 30(1), 106–128 (2012). https://doi.org/10.1109/msp.2011.2179329

    Article  ADS  Google Scholar 

  13. Maggioni, M., Sanchez-Monge, E., Foi, A.: IEEE Trans Image Process 23(10), 4282–4296 (2014). https://doi.org/10.1109/tip.2014.2345261

    Article  ADS  MathSciNet  Google Scholar 

  14. Lebrun, M., Colom, M., Buades, A., Morel, J.M.: Acta Numer 21, 475–576 (2012). https://doi.org/10.1017/s0962492912000062

    Article  MathSciNet  Google Scholar 

  15. Zhang, T., Li, X., Li, J., Xu, Z.: Sensors 20(19), 5567 (2020). https://doi.org/10.3390/s20195567

    Article  ADS  Google Scholar 

  16. Lukas, J., Fridrich, J., Goljan, M.: IEEE Trans Inf Forensics Secur 1(2), 205–214 (2006). https://doi.org/10.1109/tifs.2006.873602

    Article  Google Scholar 

  17. Fridrich, J.: IEEE Signal Process Mag 26(2), 26–37 (2009). https://doi.org/10.1109/msp.2008.931078

    Article  ADS  Google Scholar 

  18. Zheng, L., Jin, G., Xu, W., Qu, H., Wu, Y.: IEEE Sensors J 17(12), 3656–3668 (2017). https://doi.org/10.1109/jsen.2017.2696562

    Article  ADS  Google Scholar 

  19. Zhang, Y., Wang, G., Xu, J.: Sensors 18(7), 2276 (2018). https://doi.org/10.3390/s18072276

    Article  ADS  Google Scholar 

  20. Jeong, B.G., Kim, B.C., Moon, Y.H., Eom, I.K.: Signal Process. 96, 266–273 (2014). https://doi.org/10.1016/j.sigpro.2013.10.002

    Article  Google Scholar 

  21. European Machine Vision Association: EMVA standard 1288 standard for characterization of image sensors and cameras. https://www.emva.org/standards-technology/emva-1288/emva-standard-1288-downloads-2/, Accessed 11 June 2023

  22. Evtikhiev, N.N., Kozlov, A.V., Krasnov, V.V., Rodin, V.G., Starikov, R.S., Cheremkhin, P.A.: Comput Opt 45(2), 267–276 (2021). https://doi.org/10.18287/2412-6179-CO-815

    Article  ADS  Google Scholar 

  23. Maître, H.: From photon to pixel the digital camera. Wiley, Hoboken, NJ (2017)

    Book  Google Scholar 

  24. Nakamoto, K., Hotaka, H.: Opt Express 30(21), 37493 (2022). https://doi.org/10.1364/oe.471394

    Article  ADS  Google Scholar 

  25. Wang, Y., Wan, B., Fu, G., Su, Y.: IEEE Trans Instrum Meas 70, 1–11 (2021). https://doi.org/10.1109/tim.2021.3088484

    Article  Google Scholar 

  26. Evtikhiev, N.N., Kozlov, A.V., Krasnov, V.V., Rodin, V.G., Starikov, R.S., Cheremkhin, P.A.: Meas Tech 64(4), 296–304 (2021). https://doi.org/10.1007/s11018-021-01932-2

    Article  Google Scholar 

  27. Silva, V.D., Chesnokov, V., Larkin, D.: A novel adaptive shading correction algorithm for camera systems. Electronic Proc. IS&T Int’l. Symp. on Electronic Imaging: Digital Photography and Mobile Imaging XII (2016). https://doi.org/10.2352/issn.2470-1173.2016.18.dpmi-249

    Book  Google Scholar 

  28. Young, I.T., Gerbrands, J.J., van Vliet, L.J.: Fundamentals of image processing. Delft University of Technology, Delft (2007)

    Google Scholar 

  29. Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall (2008)

    Google Scholar 

  30. Kozlov, A.V., Rodin, V.G., Starikov, R.S., Evtikhiev, N.N., Cheremkhin, P.A.: IEEE Sensors J 23(5), 4883–4891 (2023). https://doi.org/10.1109/JSEN.2023.3238673

    Article  ADS  Google Scholar 

  31. Fienup, J.R.: Appl Opt 36(32), 8352–8357 (1997). https://doi.org/10.1364/AO.36.008352

    Article  ADS  Google Scholar 

  32. Huynh-Thu, Q., Ghanbari, M.: Electron Lett 44(13), 800–801 (2008). https://doi.org/10.1049/el:20080522

    Article  ADS  Google Scholar 

  33. Tounsi, Y., Kumar, M., Na, A.И.C.C.F.: Mendoza-Santoyo, and O. Matoba. Appl. Opt. 58(26), 7110–7120 (2019). https://doi.org/10.1364/AO.58.007110

    Article  ADS  Google Scholar 

  34. Katkovnik, V., Shevkunov, I.A., Petrov, N.V., Egiazarian, K.: Opt Lett 40(10), 2417–2420 (2015). https://doi.org/10.1364/OL.40.002417

    Article  ADS  Google Scholar 

  35. Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall, Englewood Cliffs, New Jersey (1990)

    Google Scholar 

Download references

Funding

This work was supported by the Russian Science Foundation, Grant No. 22-29-00603.

Author information

Authors and Affiliations

  1. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russian Federation

    A. V. Kozlov, N. V. Nikitin, V. G. Rodin & P. A. Cheremkhin

Authors
  1. A. V. Kozlov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. V. Nikitin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. G. Rodin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. A. Cheremkhin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. V. Kozlov.

Ethics declarations

Conflict of interest

A. V. Kozlov, N. V. Nikitin, V. G. Rodin and P.A. Cheremkhin declare that they have no competing interests.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 12, pp. 26–34, December, 2023. Russian DOI: https://doi.org/10.32446/0368-1025it.2023-12-26-34

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Original article submitted November 23, 2023

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kozlov, A.V., Nikitin, N.V., Rodin, V.G. et al. Improving the reliability of digital camera identification by optimizing the algorithm for comparing noise signatures. Meas Tech (2024). https://doi.org/10.1007/s11018-024-02308-y

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11018-024-02308-y

Keywords

Search

Navigation