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A digitally altered image, often leaving no visual clues of
having been tampered with, can be indistinguishable from an authentic
image. The tampering, however, may disturb some underlying statistical
properties of the image. Under this assumption, we propose five
techniques that quantify and detect statistical perturbations found in
different forms of tampered images: (1) re-sampled images (e.g.,
scaled or rotated); (2) manipulated color filter array interpolated
images; (3) double JPEG compressed images; (4) images with duplicated
regions; and (5) images with inconsistent noise patterns. These
techniques work in the absence of any embedded watermarks or
signatures. For each technique we develop the theoretical foundation,
show its effectiveness on credible forgeries, and analyze its
sensitivity and robustness to simple counter-attacks.
Ph.D. thesis, advisor: Hany Farid.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Alin C. Popescu, "Statistical Tools for Digital Image Forensics." Dartmouth Computer Science Technical Report TR2005-531, December 2004.
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