Image forgery detection based on the fusion of machine learning and block-matching methods
This work addresses image forgery detection, a domain-specific problem in digital forensics, with incremental improvements in handling small forgeries.
The paper tackled the problem of detecting small image forgeries by developing a new detector that fuses machine learning descriptors with a copy-move method, resulting in reduced missing detection rates and extremely encouraging overall performance.
Dense local descriptors and machine learning have been used with success in several applications, like classification of textures, steganalysis, and forgery detection. We develop a new image forgery detector building upon some descriptors recently proposed in the steganalysis field suitably merging some of such descriptors, and optimizing a SVM classifier on the available training set. Despite the very good performance, very small forgeries are hardly ever detected because they contribute very little to the descriptors. Therefore we also develop a simple, but extremely specific, copy-move detector based on region matching and fuse decisions so as to reduce the missing detection rate. Overall results appear to be extremely encouraging.