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.
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References
Daugman JG (1989) Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Trans Biomed Eng 36(1):107–114
Kersten D (1987) Predictability and redundancy of natural images. J Opt Soc Am A, 4(12):2395–2400
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Mach Intell 11:674–693
Westfeld A, F5. www.wrn.inf.tu-dresden.de/westfeld/f5
Vaidyanathan PP (1987) Quadrature mirror filter banks, M-band extensions and perfect recon- struction techniques. IEEE ASSP Mag 4(3):4–20
Vetterli M (1987) A theory of multirate filter banks. IEEE Trans ASSP, 35(3):356–372
Upham D, Jsteg. http://ftp.funet.fi
Hetzl S, Steghide. http://steghide.sourceforge.net
Latham A, Jpeg Hide-and-Seek. http://linux01.gwdg.de/alatham/stego
Provos N, Honeyman P (2002) Detecting steganographic content on the internet. In ISOC NDSSf02, San Diego, CA
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge
Ruderman DL, Bialek W (1994) Statistics of natural image: scaling in the woods. Phys Rev Lett 73(6):814–817
Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4(12):2379–2394
Buccigrossi RW, Simoncelli EP (1999) Image compression via joint statistical characteri- zation in the wavelet domain. IEEE Trans Image Process 8(12):1688–1701
Gluckman J (2003) On the use of marginal statistics of subband images. In IEEE international conference on computer vision, Nice, France
Foley J, van Dam A, Feiner S, Hughes J (1997) Computer graphics: principles and practice. Addison-Wesley
Anderson RJ, Petitcolas FAP (1998) On the limits of steganography. IEEE J Sel Areas Commun 16(4):474–481
Johnson N, Jajodia S (1998) Exploring steganography: seeing the unseen. IEEE Comput. 31(2):26–34
Kahn D (1996) The history of steganography. In proceedings of information hiding, First inter- national workshop, Cambridge, UK
Petitcolas EAP, Anderson RJ, Kuhn MG (1999) Information hiding—a survey. Proc IEEE 87(7):1062–1078
Fridrich J, Goljan M, Hogea D (2003) New methodology for breaking steganographic techniques for JPEGs. In SPIE Symposium on Electronic Imaging, Santa Clara, CA
Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley, New York
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Lyu, S. (2013). Natural Image Statistics in Digital Image Forensics. In: Sencar, H., Memon, N. (eds) Digital Image Forensics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0757-7_8
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DOI: https://doi.org/10.1007/978-1-4614-0757-7_8
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