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We describe a set of natural image statistics that are built upon two
multi-scale image decompositions, the quadrature mirror filter pyramid
decomposition and the local angular harmonic decomposition. These
image statistics consist of first- and higher-order statistics that
capture certain statistical regularities of natural images. We propose
to apply these image statistics, together with classification
techniques, to three problems in digital image forensics: (1)
differentiating photographic images from computer-generated
photorealistic images, (2) generic steganalysis; (3) rebroadcast image
detection. We also apply these image statistics to the traditional
art authentication for forgery detection and identification of artists
in an art work. For each application we show the effectiveness of these
image statistics and analyze their sensitivity and robustness.
Ph.D dissertation. Advisor: Hany Farid.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
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Siwei Lyu, "Natural Image Statistics for Digital Image Forensics." Dartmouth Computer Science Technical Report TR2005-557, August 2005.
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