Panchromatic Imagery Copy-Paste Localization Through Data-Driven Sensor Attribution

Overhead images can be obtained using different acquisition and processing techniques, and they are becoming more and more popular. As with common photographs, they can be forged and manipulated by malicious users. However, not all image forensics methods tailored to normal photos can be successfully applied out of the box to overhead images. In this paper we consider the problem of localizing copy-paste forgeries on panchromatic images acquired with different satellites. We leverage a set of Convolutional Neural Networks (CNNs) that extract traces of the acquisition satellite directly from image patches. We then determine whether an image region appears to have been acquired with a different satellite than the rest of the picture. Results show that the proposed technique outperforms more sophisticated image forensics tools tailoring common photographs.