  The widespread availability of image editing tools and improvements in image
processing techniques allow image manipulation to be very easy. Oftentimes,
easy-to-use yet sophisticated image manipulation tools yields
distortions/changes imperceptible to the human observer. Distribution of forged
images can have drastic ramifications, especially when coupled with the speed
and vastness of the Internet. Therefore, verifying image integrity poses an
immense and important challenge to the digital forensic community. Satellite
images specifically can be modified in a number of ways, including the
insertion of objects to hide existing scenes and structures. In this paper, we
describe the use of a Conditional Generative Adversarial Network (cGAN) to
identify the presence of such spliced forgeries within satellite images.
Additionally, we identify their locations and shapes. Trained on pristine and
falsified images, our method achieves high success on these detection and
localization objectives.
