Adversarial Examples in Remote Sensing

TL;DR

This paper investigates adversarial attacks on machine learning models used in remote sensing, specifically satellite image classification, highlighting unique challenges and practical considerations for real-world attack scenarios.

Contribution

It introduces a new study of adversarial examples in satellite image classification with a focus on multi-temporal observations and practical attack considerations.

Findings

Preliminary analysis of adversarial examples in satellite data.

Consideration of multi-temporal observations in attack scenarios.

Discussion of practical challenges for real-world adversaries.

Abstract

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.