Skip to main content

Image Noise and Digital Image Forensics

  • Conference paper
  • First Online:
Book cover Digital-Forensics and Watermarking (IWDW 2015)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9569))

Included in the following conference series:

Abstract

Noise is an intrinsic specificity of all forms of imaging, and can be found in various forms in all domains of digital imagery. This paper offers an overall review of digital image noise, from its causes and models to the degradations it suffers along the image acquisition pipeline. We show that by the end of the pipeline, the noise may have widely different characteristics compared to the raw image, and consider the consequences in forensic and counter-forensic imagery.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. LibRaw 0.17. Image decoder library (2015)

    Google Scholar 

  2. Buades, A., Coll, B., Morel, J.-M.: Non-local means denoising. Image Process. Line (2011)

    Google Scholar 

  3. Chen, M., Fridrich, J., Goljan, M., Lukáš, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)

    Article  Google Scholar 

  4. Colom, M., Buades, A.: Analysis and extension of the PCA method, estimating a noise curve from a single image. Image Process. Line (2014)

    Google Scholar 

  5. Costantini, R., Susstrunk, S.: Virtual sensor design. In: Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V (2004)

    Google Scholar 

  6. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  7. Dehnie, S., Sencar, T., Memon, N.: Digital image forensics for identifying computer generated and digital camera images. In: 2006 IEEE International Conference on Image Processing, pp. 2313–2316. IEEE (2006)

    Google Scholar 

  8. Fan, W., Wang, K., Cayre, F., Xiong, Z.: A variational approach to JPEG anti-forensics. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3058–3062. IEEE (2013)

    Google Scholar 

  9. Faraji, H., MacLean, W.J.: CCD noise removal in digital images. IEEE Trans. Image Process. 15, 2676–2685 (2006)

    Article  Google Scholar 

  10. Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)

    Article  MathSciNet  Google Scholar 

  11. Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical Poissonian-Gaussian noise modeling and fitting for single image raw-data. IEEE Trans. Image Process. 17(10), 1737–1754 (2008)

    Article  MathSciNet  Google Scholar 

  12. Fridrich, J.: Digital image forensics using sensor noise. Signal Process. Mag. 26(2), 26–37 (2009)

    Article  Google Scholar 

  13. Hou, J.-U., Jang, H.-U., Lee, H.-K.: Hue modification estimation using sensor pattern noise. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5287–5291. IEEE (2014)

    Google Scholar 

  14. Irie, K., McKinnon, A.E., Unsworth, K., Woodhead, I.M.: A model for measurement of noise in CCD digital-video cameras. Meas. Sci. Technol. 19, 045207 (2008)

    Article  Google Scholar 

  15. Jezierska, A., Chaux, C., Pesquet, J.-C., Talbot, H.: An EM approach for Poisson-Gaussian noise modeling. In: EUSIPCO, pp. 2244–2248, August 2011

    Google Scholar 

  16. Jezierska, A., Chouzenoux, E., Pesquet, J.-C., Talbot, H.: A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), March 2012

    Google Scholar 

  17. Lawgaly, A., Khelifi, F., Bouridane, A.: Weighted averaging-based sensor pattern noise estimation for source camera identification. In: IEEE International Conference on Image Processing (ICIP 2014), pp. 5357–5361, October 2014

    Google Scholar 

  18. Lukac, R.: Single-Sensor Imaging: Methods and Applications for Digital Cameras. CRC Press, Boca Raton (2008)

    Book  Google Scholar 

  19. Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27, 1497–1503 (2009)

    Article  Google Scholar 

  20. Medkeff, J.: Using image calibration to reduce digital noise in images (2004)

    Google Scholar 

  21. Nozick, V.: Camera array image rectification and calibration for stereoscopic and autostereoscopic displays. Ann. Telecommun. 68(11), 581–596 (2013)

    Article  Google Scholar 

  22. Paliy, D., Katkovnik, V., Bilcu, R., Alenius, S., Egiazarian, K.: Spatially adaptive color filter array interpolation for noiseless and noisy data. Int. J. Imaging Syst. Technol. 17, 105–122 (2007)

    Article  Google Scholar 

  23. Pan, X., Zhang, X., Lyu, S.: Exposing image splicing with inconsistent local noise variances. In: International Conference on Computation Photography (ICCP), pp. 1–10, April 2012

    Google Scholar 

  24. Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 128–147. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  25. Rosenfeld, K., Sencar, H.T.: A study of the robustness of PRNU-based camera identification. In: IS&T/SPIE Electronic Imaging, p. 72540M. International Society for Optics and Photonics (2009)

    Google Scholar 

  26. Stamm, M., Liu, K.J.R.: Blind forensics of contrast enhancement in digital images. In: 2008 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 3112–3115. IEEE (2008)

    Google Scholar 

  27. Stamm, M.C., Tjoa, S.K., Lin, W.S., Liu, K.J.R.: Anti-forensics of JPEG compression. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1694–1697. IEEE (2010)

    Google Scholar 

  28. Valenzise, G., Nobile, V., Tagliasacchi, M., Tubaro, S.: Countering JPEG anti-forensics. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 1949–1952. IEEE (2011)

    Google Scholar 

  29. Vaseghi, S.V.: Advanced Digital Signal Processing and Noise Reduction. Wiley, New York (2008)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thibaut Julliand .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Julliand, T., Nozick, V., Talbot, H. (2016). Image Noise and Digital Image Forensics. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31960-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31959-9

  • Online ISBN: 978-3-319-31960-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics