Abstract

Image splicing is very common and fundamental in image tampering, which severely threatens the integrity and authenticity of images. As a result, there is a great need for the detection of image splicing. In this paper, an improved run length based scheme is proposed to detect this specific artifact. Firstly, the edge gradient matrix of an image is computed. Secondly, approximate run length is defined and calculated along the edge gradient direction. Thirdly, features are constructed from the related histograms of the approximate run length. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the constructed features. The experiment results demonstrate that the proposed approach can achieve a moderate accuracy with far less computational cost and much fewer features when compared with a similar method.

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