  New transformer networks have been integrated into object tracking pipelines
and have demonstrated strong performance on the latest benchmarks. This paper
focuses on understanding how transformer trackers behave under adversarial
attacks and how different attacks perform on tracking datasets as their
parameters change. We conducted a series of experiments to evaluate the
effectiveness of existing adversarial attacks on object trackers with
transformer and non-transformer backbones. We experimented on 7 different
trackers, including 3 that are transformer-based, and 4 which leverage other
architectures. These trackers are tested against 4 recent attack methods to
assess their performance and robustness on VOT2022ST, UAV123 and GOT10k
datasets. Our empirical study focuses on evaluating adversarial robustness of
object trackers based on bounding box versus binary mask predictions, and
attack methods at different levels of perturbations. Interestingly, our study
found that altering the perturbation level may not significantly affect the
overall object tracking results after the attack. Similarly, the sparsity and
imperceptibility of the attack perturbations may remain stable against
perturbation level shifts. By applying a specific attack on all transformer
trackers, we show that new transformer trackers having a stronger
cross-attention modeling achieve a greater adversarial robustness on tracking
datasets, such as VOT2022ST and GOT10k. Our results also indicate the necessity
for new attack methods to effectively tackle the latest types of transformer
trackers. The codes necessary to reproduce this study are available at
https://github.com/fatemehN/ReproducibilityStudy.
