Abstract
As an important branch of digital image forensics, image splicing is the most fundamental step in photomontage. In the present paper, an efficient blind digital forensics method for image splicing localization and forgery detection is proposed. The method is based on the estimated natural counterpart of the spliced image using different quality re-demosaicing approaches. By comparing the test image with its estimated natural counterpart, the abrupt edges along the spliced region are exposed and a binary image is obtained to illustrate the localization of the splicing. The features extracted from the binary image are fed into a support vector machine classifier to detect spliced forgeries. The DVMM uncompressed spliced image database is used to evaluate the performance of the proposed method. The experimental results show the effectiveness of the method on splicing localization and its accuracy on forgery detection.
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