  A growing number of commercial satellite companies provide easily accessible
satellite imagery. Overhead imagery is used by numerous industries including
agriculture, forestry, natural disaster analysis, and meteorology. Satellite
images, just as any other images, can be tampered with image manipulation
tools. Manipulation detection methods created for images captured by "consumer
cameras" tend to fail when used on satellite images due to the differences in
image sensors, image acquisition, and processing. In this paper we propose an
unsupervised technique that uses a Vision Transformer to detect spliced areas
within satellite images. We introduce a new dataset which includes manipulated
satellite images that contain spliced objects. We show that our proposed
approach performs better than existing unsupervised splicing detection
techniques.
