A feature-based approach for image tampering detection and localization
We propose a new camera-based technique for tampering localization. A large number of blocks are extracted off-line from training images and characterized through features based on a dense local descriptor. A multidimensional Gaussian model is then fit to the training features. In the testing phase, the image is analyzed in sliding-window modality: for each block, the log-likelihood of the associated feature is computed, reprojected in the image domain, and aggregated, so as to form a smooth decision map. Eventually, the tampering is localized by simple thresholding. Experiments carried out in a number of situation of interest show promising results.