Yonghao Xu, Weikang Yu, and Pedram Ghamisi
An illustration of the proposed adversarial defense framework.
Deep learning models have achieved state-of-the-art performance in the interpretation of geoscience and remote sensing data. However, their vulnerability to adversarial attacks should not be neglected. To address this challenge, we propose a task-guided denoising network to conduct adversarial defense for the remote sensing scene classification task in this study. Specifically, given an adversarial remote sensing image, we use a denoising network to transform it as close to its corresponding clean image as possible with the constraint of the appearance loss. Besides, to further correct the predicted logits, the perceptual loss and the classification loss are adopted with the aid of a pre-trained classification network with fixed weights. Despite its simplicity, extensive experiments on the UAE-RS (universal adversarial examples in remote sensing) dataset demonstrate that the proposed method can significantly improve the resistibility of different deep learning models against the adversarial examples.
CEUR Workshop Proceedings, 3207, 2022-07-25.