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Yu-Feng Hsu, Shih-Fu Chang. Detecting Image Splicing Using Geometry Invariants And Camera Characteristics Consistency. In Interational Conference on Multimedia and Expo (ICME), Toronto, Canada, July 2006.

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Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image


Yu-Feng Hsu
Shih-Fu Chang

BibTex Reference

   Author = {Hsu, Yu-Feng and Chang, Shih-Fu},
   Title = {Detecting Image Splicing Using Geometry Invariants And Camera Characteristics Consistency},
   BookTitle = {Interational Conference on Multimedia and Expo (ICME)},
   Address = {Toronto, Canada},
   Month = {July},
   Year = {2006}

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