Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier

TL;DR

This paper introduces a novel satellite image forgery detection method using GANs for feature learning and one-class SVMs for anomaly detection, effective without needing forged training examples.

Contribution

It develops a new forensic approach tailored for satellite images, addressing the challenge of detecting forgeries without prior examples of forgeries.

Findings

Effective detection of forgeries of various sizes and shapes

Works without requiring forged images for training

Validated on different satellite image datasets

Abstract

Current satellite imaging technology enables shooting high-resolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape.