  Deep neural networks (DNNs) have risen to prominence as key solutions in
numerous AI applications for earth observation (AI4EO). However, their
susceptibility to adversarial examples poses a critical challenge, compromising
the reliability of AI4EO algorithms. This paper presents a novel Universal
Adversarial Defense approach in Remote Sensing Imagery (UAD-RS), leveraging
pre-trained diffusion models to protect DNNs against universal adversarial
examples exhibiting heterogeneous patterns. Specifically, a universal
adversarial purification framework is developed utilizing pre-trained diffusion
models to mitigate adversarial perturbations through the introduction of
Gaussian noise and subsequent purification of the perturbations from
adversarial examples. Additionally, an Adaptive Noise Level Selection (ANLS)
mechanism is introduced to determine the optimal noise level for the
purification framework with a task-guided Frechet Inception Distance (FID)
ranking strategy, thereby enhancing purification performance. Consequently,
only a single pre-trained diffusion model is required for purifying universal
adversarial samples with heterogeneous patterns across each dataset,
significantly reducing training efforts for multiple attack settings while
maintaining high performance without prior knowledge of adversarial
perturbations. Experimental results on four heterogeneous RS datasets, focusing
on scene classification and semantic segmentation, demonstrate that UAD-RS
outperforms state-of-the-art adversarial purification approaches, providing
universal defense against seven commonly encountered adversarial perturbations.
Codes and the pre-trained models are available online
(https://github.com/EricYu97/UAD-RS).
