Remote Sensing Image Forgery Detection Using Split and Merge Input U-Net

Remote sensing images are indispensable tools in critical domains such as environmental monitoring, urban planning, and national security. However, the integrity of these data sources can be compromised due to forgery, leading to mis-information and potentially harmful consequences. This paper presents a novel approach for detecting forgery in remote sensing images. Through extensive experimentation on benchmark datasets and the RSICD (Remote Sensing Image Capturing Dataset) [1], augmented with synthetic data created using forgery techniques such as copy-move, resizing, rotation and splicing, we demonstrate the effectiveness and scalability of our approach. With the variations in U-Net model and with split and merge operations our method is able to locate the forged regions of input images efficiently.