# Contextual adversarial attack against aerial detection in the physical world

## TL;DR

This paper introduces a novel physical-world adversarial attack method targeting aerial detection systems, exploiting contextual information to effectively deceive detectors without obscuring objects, demonstrating high transferability and practicality.

## Contribution

It pioneers a contextual adversarial attack approach against aerial detection in real-world scenarios, leveraging target context to improve attack success and transferability.

## Key findings

- The attack effectively deceives various aerial detectors in physical settings.
- Using contextual information enhances attack transferability and success.
- The method demonstrates strong physical practicality and robustness.

## Abstract

Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs' sensitivity and vulnerability to maliciously elaborated adversarial examples have progressively garnered attention. Recently, physical attacks have gradually become a hot issue due to they are more practical in the real world, which poses great threats to some security-critical applications. In this paper, we take the first attempt to perform physical attacks in contextual form against aerial detection in the physical world. We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects to hide. Based on the findings that the targets' contextual information plays an important role in aerial detection by observing the detectors' attention maps, we propose to make full use of the contextual area of the interested targets to elaborate contextual perturbations for the uncovered attacks in real scenarios. Extensive proportionally scaled experiments are conducted to evaluate the effectiveness of the proposed contextual attack method, which demonstrates the proposed method's superiority in both attack efficacy and physical practicality.