Natural Weather-Style Black-Box Adversarial Attacks Against Optical Aerial Detectors

Most existing adversarial attack methods against detectors involve adding adversarial perturbations to benign images to synthesize adversarial examples. However, directly applying these methods, originally designed for natural image detectors, to optical aerial image detectors can lead to perturbations that appear unnatural and suspicious to human eyes, owing to intrinsic dissimilarities between these two types of images. Inspired by the fact that the captured optical aerial images are heavily affected by weather conditions, this article proposes a novel method for conducting adversarial attacks against optical aerial detectors by leveraging natural weather-style perturbations. Compared to existing methods, our scheme produces more natural and stealthy adversarial examples. To enhance the practicality of the proposed method in real-world scenarios, we implement the attacks in black-box settings where only the model’s predictions are accessible. Specifically, we formulate the generation of adversarial weather perturbations in black-box as an optimization problem and effectively solve it using the differential evolution (DE) algorithm. Through extensive experiments, we verify the effectiveness of our method and investigate the transferability of generated adversarial examples across different models. In light of the significant generalization and effectiveness of our method, we generate and release the first dataset with adversarial weather-style perturbations based on the DOTA dataset, which we abbreviate as DOTA-W. This dataset serves as a valuable resource for evaluating and improving the robustness of optical aerial detectors. The code and dataset have been released at
https://github.com/tang-agui/attADs-AWP
.