Adversarial Examples in Visual Object Tracking in Satellite Videos: Cross-Frame Momentum Accumulation for Adversarial Examples Generation

Abstract

The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. This article first explores the impact of adversarial examples in the field of visual object tracking in remote sensing imagery. We design a classification- and regression-based loss function for the popular Siamese RPN series of visual object tracking models and use the PGD gradient-based attack method to generate adversarial examples. Additionally, we consider the temporal consistency of video frames and design an adversarial examples attack method based on momentum continuation. We evaluate our method on the remote sensing visual object tracking datasets SatSOT and VISO and the traditional datasets OTB100 and UAV123. The experimental results show that our approach can effectively reduce the performance of the tracker.