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.
Download paper:
Adobe portable document (pdf) 
Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
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
@InProceedings{hsu:06ICMEcrf,
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}
}
Get EndNote Reference (.ref)
For problems or questions regarding this web site contact The
Web Master.
This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).
