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.
A semi-automatic method for the detection of image splicing based on geometry invariants and camera characteristic consistency have been proposed by Hsu REF .
Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
2006 IEEE International Conference on Multimedia and Expo
2006 IEEE International Conference on Multimedia and Expo
Computer Science