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On the influence of denoising in PRNU based forgery detection

Published:29 October 2010Publication History

ABSTRACT

To detect some image forgeries one can rely on the Photo-Response Non-Uniformity (PRNU), a deterministic pattern associated with each individual camera, which can be loosely modeled as low-intensity multiplicative noise. A very promising algorithm for PRNU-based forgery detection has been recently proposed by Chen et al. Image denoising is a key step of the algorithm, since it allows to single out and remove most of the signal components and reveal the PRNU pattern. In this work we analyze the influence of denoising on the overall performance of the method and show that the use of a suitable state-of-the art denoising technique improves performance appreciably w.r.t. the original algorithm.

References

  1. I.Amerini, R.Caldelli, V.Cappellini, F.Picchioni, and A.Piva. Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification. In Proceedings of Digital Signal Processing, pages 1--7, Santorini, Greece, Jul. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A.Buades, B.Coll, and J.M.Morel. A review of image denoising algorithms,with a new one. Multiscale Model. Simul., 4(2):490--530. July 2005.Google ScholarGoogle ScholarCross RefCross Ref
  3. M.Chen, J.Fridrich, M.Goljan, and J.Luká. Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security, 3(1):74--90, Mar. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. K.Dabov, A.Foi, V.Katkovnik, and K.Egiazarian. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8):2080--2095, Aug. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J.Fridrich. Digital image forensics. IEEE Signal Processing Magazine, 26(2):26--37, Mar. 2009.Google ScholarGoogle ScholarCross RefCross Ref
  6. J.Fridrich, D.Soukal, and J.Lukás. Detection of copy move forger in digital images. In Proc. Digital Forensic Research Workshop. 2003.Google ScholarGoogle Scholar
  7. R.G.E.Healey. Radiometric ccd camera calibration and noise estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(3):267--275, Mar. 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J.He, Z.Lin, L.Wang, and X.Tang. Detecting doctored jpeg images via dct coefficient analysis. In Proc. of 9th European Conference on Computer Vision, pages 423--435, Graz, Austria, May 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J.Lukas, J.Fridrich, and M.Goljan. Detecting digital image forgeries using sensor pattern noise. In SPIE Electronic Imaging Forensics, Security, Steganography, and Watermarking of Multimedia Contents VIII, volume 6072, pages 362--372, San Jose, CA, USA, Jan. 2006.Google ScholarGoogle Scholar
  10. M.K.Mihcak, I.Kozintsev, and K.Ramchandran. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3253--3256, Phoenix, AZ, USA, Mar. 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A.C.Popescu and H.Farid. Exposing digital forgeries by detecting duplicated image regions. Dept. Comput. sci., Dartmouth College. Tech. Rep. TR2004--515, 2004.Google ScholarGoogle Scholar
  12. A.C.Popescu and H.Farid. Exposing digital forgeries by detecting traces of re-sampling. IEEE Transactions on Signal Processing. 53(2):758--767, Feb. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S.Ye, Q.Sun, and E.C. Chang. Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In Proc. IEEE Int. Conf. Multimedia and Expo, pages 12--15, Beijing, China, 2007.Google ScholarGoogle ScholarCross RefCross Ref

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      • Published in

        cover image ACM Conferences
        MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
        October 2010
        134 pages
        ISBN:9781450301572
        DOI:10.1145/1877972

        Copyright © 2010 ACM

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        Publication History

        • Published: 29 October 2010

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