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Natural Image Statistics in Digital Image Forensics

  • Siwei Lyu
Chapter

Abstract

The fundamental problem in digital image forensics is to differentiate tampered images from those of untampered ones. A general solution framework can be obtained using the statistical properties of natural photographic images. In the recent years, applications of natural image statistics in digital image forensics have witnessed rapid developments and led to promising results. In this chapter, we provide an overview of recent developments of natural image statistics, and focus on three applications of natural image statistics in digital image forensics as (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection.

Keywords

Cover Image Natural Image Linear Predictor Synthetic Image Stego Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Daugman JG (1989) Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Trans Biomed Eng 36(1):107–114CrossRefGoogle Scholar
  2. 2.
    Kersten D (1987) Predictability and redundancy of natural images. J Opt Soc Am A, 4(12):2395–2400Google Scholar
  3. 3.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Mach Intell 11:674–693MATHCrossRefGoogle Scholar
  4. 4.
  5. 5.
    Vaidyanathan PP (1987) Quadrature mirror filter banks, M-band extensions and perfect recon- struction techniques. IEEE ASSP Mag 4(3):4–20Google Scholar
  6. 6.
    Vetterli M (1987) A theory of multirate filter banks. IEEE Trans ASSP, 35(3):356–372Google Scholar
  7. 7.
    Upham D, Jsteg. http://ftp.funet.fi
  8. 8.
  9. 9.
    Latham A, Jpeg Hide-and-Seek. http://linux01.gwdg.de/alatham/stego
  10. 10.
    Provos N, Honeyman P (2002) Detecting steganographic content on the internet. In ISOC NDSSf02, San Diego, CAGoogle Scholar
  11. 11.
    Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. CambridgeGoogle Scholar
  12. 12.
    Ruderman DL, Bialek W (1994) Statistics of natural image: scaling in the woods. Phys Rev Lett 73(6):814–817CrossRefGoogle Scholar
  13. 13.
    Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4(12):2379–2394CrossRefGoogle Scholar
  14. 14.
    Buccigrossi RW, Simoncelli EP (1999) Image compression via joint statistical characteri- zation in the wavelet domain. IEEE Trans Image Process 8(12):1688–1701CrossRefGoogle Scholar
  15. 15.
    Gluckman J (2003) On the use of marginal statistics of subband images. In IEEE international conference on computer vision, Nice, FranceGoogle Scholar
  16. 16.
    Foley J, van Dam A, Feiner S, Hughes J (1997) Computer graphics: principles and practice. Addison-WesleyGoogle Scholar
  17. 17.
    Anderson RJ, Petitcolas FAP (1998) On the limits of steganography. IEEE J Sel Areas Commun 16(4):474–481CrossRefGoogle Scholar
  18. 18.
    Johnson N, Jajodia S (1998) Exploring steganography: seeing the unseen. IEEE Comput. 31(2):26–34Google Scholar
  19. 19.
    Kahn D (1996) The history of steganography. In proceedings of information hiding, First inter- national workshop, Cambridge, UKGoogle Scholar
  20. 20.
    Petitcolas EAP, Anderson RJ, Kuhn MG (1999) Information hiding—a survey. Proc IEEE 87(7):1062–1078CrossRefGoogle Scholar
  21. 21.
    Fridrich J, Goljan M, Hogea D (2003) New methodology for breaking steganographic techniques for JPEGs. In SPIE Symposium on Electronic Imaging, Santa Clara, CAGoogle Scholar
  22. 22.
    Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.AlbanyUSA

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