Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.

Keywords

Image forgery Noise Raw image 

References

  1. 1.
    LibRaw-0.17: Image decoder library (2015). www.libraw.org
  2. 2.
    Bayram, S., Avcibas, I., Sankur, B., Memon, N.D.: Image manipulation detection. Electron. Imaging 15(4), 1–17 (2006)CrossRefGoogle Scholar
  3. 3.
    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)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Farid, H.: A survey of image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)CrossRefGoogle Scholar
  5. 5.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)CrossRefGoogle Scholar
  6. 6.
    Finlayson, G., Shiele, B., Crowley, J.: Comprehensive colour normalization. In: Proceedings European Conference on Computer Vison, vol. I, pp. 475–490 (1998)Google Scholar
  7. 7.
    Fu, D., Shi, Y.Q., Su, W.: Image splicing detection using 2D phase congruency and statistical moments of characteristic function. In: Proceedings of SPIE Security, Steganography, and Watermarking of Multimedia Contents IX (2007)Google Scholar
  8. 8.
    He, J., Lin, Z., Wang, L., Tang, X.: Detecting doctored JPEG images via DCT coefficient analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 423–435. Springer, Heidelberg (2006). doi:10.1007/11744078_33 CrossRefGoogle Scholar
  9. 9.
    Adobe Systems Incorporated: Digital negative (DNG) specification, version 1.4.0.0 (2012)Google Scholar
  10. 10.
    Julliand, T., Nozick, V., Talbot, H.: Automated image splicing detection from noise estimation in raw images. In: Imaging for Crime Prevention and Detection, pp. 1–6 (2015)Google Scholar
  11. 11.
    Lin, Z., He, J., Tang, X., Tang, C.: Fast, automatic and fine-grained tampered JPEG images detection via DCT coefficient analysis. Pattern Recogn. 42(11), 2492–2501 (2009)CrossRefMATHGoogle Scholar
  12. 12.
    Lukáš, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Proceedings SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, pp. 0Y1-0Y11 (2006)Google Scholar
  13. 13.
    Mahdian, B., Saic, S.: Detection of resampling supplemented with noise inconsistencies analysis for image forensics. In: International Conference on Computational Sciences and its Applications, pp. 546–556, July 2008Google Scholar
  14. 14.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27, 1497–1503 (2009)CrossRefGoogle Scholar
  15. 15.
    Pan, X., Zhang, X., Lyu, S.: Exposing image forgery with blind noise estimation. In: The 13th ACM Workshop on Multimedia and Security, Buffalo, NY (2011)Google Scholar
  16. 16.
    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 2012Google Scholar
  17. 17.
    Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: 6th International Workshop on Information Hiding (2004)Google Scholar
  18. 18.
    Popescu, C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Signal Process. 53(10), 1948–3959 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Thibault Julliand
    • 1
  • Vincent Nozick
    • 2
  • Hugues Talbot
    • 1
  1. 1.Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE Paris, UPEMNoisy-le-GrandFrance
  2. 2.Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE Paris, UPEMMarne-la-ValléeFrance

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