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Dense local descriptors and machine learning have been used with success inseveral applications, like classification of textures, steganalysis, andforgery detection. We develop a new image forgery detector building upon somedescriptors recently proposed in the steganalysis field suitably merging someof such descriptors, and optimizing a SVM classifier on the available trainingset. Despite the very good performance, very small forgeries are hardly everdetected because they contribute very little to the descriptors. Therefore wealso develop a simple, but extremely specific, copy-move detector based onregion matching and fuse decisions so as to reduce the missing detection rate.Overall results appear to be extremely encouraging.

Image forgery detection based on the fusion of machine learning and block-matching methods