Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images

Abstract

Recent studies have shown that deep-learning-based models for processing Unmanned Aerial Vehicle (UAV) remote sensing images are vulnerable to artificially designed adversarial examples, which can lead to incorrect predictions of deep models when facing adversarial examples. Previous adversarial attack methods have mainly focused on the classification and detection of UAV remote sensing images, and there is still a lack of research on adversarial attacks for object tracking in UAV video. To address this challenge, we propose an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline. First, we employ an attention feature loss to make the original frame’s features dissimilar to those of the adversarial frame, and an attention confidence loss to either suppress or enhance different confidence scores. Additionally, by forcing the tracker to concentrate on the background information near the target, a background distraction loss is used to mismatch templates with subsequent frames. Finally, we add total variation loss to generate adversarial examples that appear natural to humans. We validate the effectiveness of our method against popular trackers such as SiamRPN, DaSiamRPN, and SiamRPN++ on the UAV123 remote sensing dataset. Experimental results verify the superior attack performance of our proposed method.