# Manipulation Detection in Satellite Images Using Deep Belief Networks

arXiv: https://arxiv.org/abs/2004.12441 · HTML: https://mlsanity.com/paper/2004.12441/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images](https://mlsanity.com/paper/2209.08984/) (2022)
- [A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images](https://mlsanity.com/paper/2305.11795/) (2023)
- [Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset](https://mlsanity.com/paper/2401.06998/) (2024)
- [GAN-Based Object Removal in High-Resolution Satellite Images](https://mlsanity.com/paper/2301.11726/) (2023)
- [HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation](https://mlsanity.com/paper/2307.11052/) (2023)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)

## Abstract (arXiv)

Satellite images are more accessible with the increase of commercial satellites being orbited. These images are used in a wide range of applications including agricultural management, meteorological prediction, damage assessment from natural disasters, and cartography. Image manipulation tools including both manual editing tools and automated techniques can be easily used to tamper and modify satellite imagery. One type of manipulation that we examine in this paper is the splice attack where a region from one image (or the same image) is inserted (spliced) into an image. In this paper, we present a one-class detection method based on deep belief networks (DBN) for splicing detection and localization without using any prior knowledge of the manipulations. We evaluate the performance of our approach and show that it provides good detection and localization accuracies in small forgeries compared to other approaches.