# Adversarial Examples in Remote Sensing

arXiv: https://arxiv.org/abs/1805.10997 · HTML: https://mlsanity.com/paper/1805.10997/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 19 of 19)

- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Benchmarking Adversarial Patch Against Aerial Detection](https://mlsanity.com/paper/2210.16765/) (2022)
- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)
- [Visual Adversarial Attacks and Defenses in the Physical World: A Survey](https://mlsanity.com/paper/2211.01671/) (2022)
- [Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2302.06060/) (2023)
- [Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?](https://mlsanity.com/paper/2108.07229/) (2021)
- [Backdoor Attacks for Remote Sensing Data with Wavelet Transform](https://mlsanity.com/paper/2211.08044/) (2022)
- [Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks](https://mlsanity.com/paper/2505.08022/) (2025)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [Responsible AI for Earth Observation](https://mlsanity.com/paper/2405.20868/) (2024)
- [Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner](https://mlsanity.com/paper/2105.14693/) (2021)
- [Cloud Adversarial Example Generation for Remote Sensing Image Classification](https://mlsanity.com/paper/2409.14240/) (2024)
- [Universal adversarial perturbation for remote sensing images](https://mlsanity.com/paper/2202.10693/) (2022)
- [On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing](https://mlsanity.com/paper/2501.11462/) (2025)
- [Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification](https://mlsanity.com/paper/2604.14643/) (2026)
- [Digital-to-Physical Transfer of Adversarial Patches for Aerial Vehicle Detection](https://mlsanity.com/paper/2606.00159/) (2026)
- [On the Adversarial Robustness of Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2608.15267/) (2026)

## Abstract (arXiv)

This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In particular, we present a new study of adversarial examples in the context of satellite image classification problems. Using a recently curated data set and associated classifier, we provide a preliminary analysis of adversarial examples in settings where the targeted classifier is permitted multiple observations of the same location over time. While our experiments to date are purely digital, our problem setup explicitly incorporates a number of practical considerations that a real-world attacker would need to take into account when mounting a physical attack. We hope this work provides a useful starting point for future studies of potential vulnerabilities in this setting.