Nested Attention U-Net: A Splicing Detection Method for Satellite Images

Abstract

Satellite imagery is becoming increasingly available due to a large number of commercial satellite companies. Many fields use satellite images, including meteorology, forestry, natural disaster analysis, and agriculture. These images can be changed or tampered with image manipulation tools causing issues in applications using these images. Manipulation detection techniques designed for images captured by “consumer cameras” tend to fail when used on satellite images. In this paper we propose a supervised method, known as Nested Attention U-Net, to detect spliced areas in the satellite images. We introduce three datasets of manipulated satellite images that contain objects generated by a generative adversarial network (GAN). We test our approach and compare it to existing supervised splicing detection and segmentation techniques and show that our proposed approach performs well in detection and localization.