Fake Images generation has become a common practice nowadays and this process has become elementary with the advent of Generative Adversarial Networks. Although though fake images are entertaining and have their benefits, they also have a few negative effects. Modern satellite images offer crucial data that aids in tracking various packages, including spatial decision, spectral properties, sensor sensitivity, and many others. Critical details needed for a few applications may be massively obtained from these photos. Yet, altering these images by removing, incorporating, or replicating objects leads to false perceptions of reality. It is our intention to come across those kind of fake satellite images. We use in-depth learning architectures to identify fake Satellite photos in order to distinguish between authentic and fake photos.