Overhead images are characterized by repetitive patterns (e.g., similar ground textures, house roofs, cars). These may introduce false positives when searching for copy-move forgeries (i.e., duplicated regions). On the other hand, a detector may be able to exploit these repetitive scenarios to derive a series of constraints to distinguish which region is original and which is the copy in case of duplication. Another approach that can be used to disambiguate the source and target image portions in a copy-move forgery considers the interpolation artefacts associated to the geometric transformations that often accompany copy-move forgeries. The non-perfect invertibility of the geometric transformation, in fact, makes it possible to predict the target region from the source one but not the other way round.

A distinguishing feature of many satellite images is their spectral resolution. In fact, the number of spectral bands of satellite images may range from 5-7 bands in multispectral images to hundreds or even thousands of bands in hyperspectral images. The correlation between spectral bands represents a unique, distinguishing signature of the sensor which acquired the image and the subsequent processing pipeline and hence can be used to understand if a portion of an image has been spliced from a donor image having a different spectral characterization. The goal of this part of the project task is to design an architecture explicitly thought to extract inter-spectral features and use it to detect spliced regions in multi- and hyper-spectral images.