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.
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References
LibRaw 0.17. Image decoder library (2015)
Buades, A., Coll, B., Morel, J.-M.: Non-local means denoising. Image Process. Line (2011)
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)
Colom, M., Buades, A.: Analysis and extension of the PCA method, estimating a noise curve from a single image. Image Process. Line (2014)
Costantini, R., Susstrunk, S.: Virtual sensor design. In: Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V (2004)
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)
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)
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)
Faraji, H., MacLean, W.J.: CCD noise removal in digital images. IEEE Trans. Image Process. 15, 2676–2685 (2006)
Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)
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)
Fridrich, J.: Digital image forensics using sensor noise. Signal Process. Mag. 26(2), 26–37 (2009)
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)
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)
Jezierska, A., Chaux, C., Pesquet, J.-C., Talbot, H.: An EM approach for Poisson-Gaussian noise modeling. In: EUSIPCO, pp. 2244–2248, August 2011
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
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
Lukac, R.: Single-Sensor Imaging: Methods and Applications for Digital Cameras. CRC Press, Boca Raton (2008)
Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27, 1497–1503 (2009)
Medkeff, J.: Using image calibration to reduce digital noise in images (2004)
Nozick, V.: Camera array image rectification and calibration for stereoscopic and autostereoscopic displays. Ann. Telecommun. 68(11), 581–596 (2013)
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)
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
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)
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)
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)
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)
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)
Vaseghi, S.V.: Advanced Digital Signal Processing and Noise Reduction. Wiley, New York (2008)
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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
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DOI: https://doi.org/10.1007/978-3-319-31960-5_1
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