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

arXiv: https://arxiv.org/abs/1802.04881 · HTML: https://mlsanity.com/paper/1802.04881/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 14 of 14)

- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2111.08685/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Splicing Detection and Localization In Satellite Imagery Using Conditional GANs](https://mlsanity.com/paper/2205.01805/) (2022)
- [An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images](https://mlsanity.com/paper/2209.08984/) (2022)
- [Amplitude SAR Imagery Splicing Localization](https://mlsanity.com/paper/2201.02409/) (2022)
- [A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images](https://mlsanity.com/paper/2305.11795/) (2023)
- [SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection](https://mlsanity.com/paper/2506.20599/) (2025)
- [SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery](https://mlsanity.com/paper/2108.12534/) (2021)
- [Detecting and Localizing Copy-Move and Image-Splicing Forgery](https://mlsanity.com/paper/2202.04069/) (2022)
- [GAN-Based Object Removal in High-Resolution Satellite Images](https://mlsanity.com/paper/2301.11726/) (2023)
- [Towards a satellite image manipulation and deepfake localization benchmark dataset](https://mlsanity.com/paper/2608.04840/) (2026)

## Abstract (arXiv)

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.