Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection

TL;DR

This paper introduces an unsupervised method using ensembles of generative autoregressive models to detect tampering in satellite images, achieving accurate localization without prior manipulation knowledge.

Contribution

It proposes a novel ensemble-based generative autoregressive approach for unsupervised satellite image manipulation detection, improving localization accuracy.

Findings

Accurate localization of manipulated regions in satellite images.

Outperforms previous methods in detection accuracy.

Effective without prior knowledge of tampering techniques.

Abstract

Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are preferred. In this paper, we use ensembles of generative autoregressive models to model the distribution of the pixels of the image in order to detect potential manipulations. We evaluate the performance of the presented approach obtaining accurate localization results compared to previously presented approaches.