Satellite images are now a widespread asset that is easily obtainable on the Web. Many portals offer these images for free, and their role in sensitive applications, such as natural disaster response, intelligence, and military, is becoming paramount. For these reasons, satellite images can be a target for malicious manipulations. Multimedia Forensics (MMF) is the discipline concerned with assessing the authenticity of multimedia data. Satellite images pose new challenges to MMF due to (i) being an inherently multimodal data asset, with some of its modalities, like Synthetic Aperture Radar (SAR) signals, which the community has never investigated; (ii) having a complex processing pipeline where forensic traces are challenging to model. In this chapter, we tackle the forensic analysis of satellite images, namely panchromatic and SAR imagery, and propose using Convolutional Neural Networks (CNNs) to extract forensic information. In particular, we will consider the problems of source attribution and image splicing localization. In both situations, CNNs prove effective and more performant than techniques developed for standard digital pictures, substantiating the need for forensic tools tailored to remote sensing data.