Exposing Postprocessed Copy-Paste Forgeries Through Transform-Invariant Features

Image manipulation has become commonplace with growing easy access to powerful computing abilities. One of the most common types of image forgeries is the copy-paste forgery, wherein a region from an image is replaced with another region from the same image. Most prior approaches to finding identical regions suffer from their inability to detect the cloned region when it has been subjected to a geometric transformation. In this paper, we propose a novel technique based on transform-invariant features. These are obtained by using the features from the MPEG-7 image signature tools. Results are provided which show the efficacy of this technique in detecting copy-paste forgeries, with translation, scaling, rotation, flipping, lossy compression, noise addition and blurring. We obtain a feature matching accuracy in excess of 90% across postprocessing operations and are able to detect the cloned regions with a high true positive rate and lower false positive rate than the state of the art.