  Satellite imagery is becoming increasingly accessible due to the growing
number of orbiting commercial satellites. Many applications make use of such
images: agricultural management, meteorological prediction, damage assessment
from natural disasters, or cartography are some of the examples. Unfortunately,
these images can be easily tampered and modified with image manipulation tools
damaging downstream applications. Because the nature of the manipulation
applied to the image is typically unknown, unsupervised methods that don't
require prior knowledge of the tampering techniques used are preferred. In this
paper, we use ensembles of generative autoregressive models to model the
distribution of the pixels of the image in order to detect potential
manipulations. We evaluate the performance of the presented approach obtaining
accurate localization results compared to previously presented approaches.
