Enhancing Tracking Robustness with Auxiliary Adversarial Defense
  Networks
Zhewei Wu Ruilong Yu Qihe Liu Shuying Cheng Shilin Qiu Shijie Zhou
Adversarial attacks in visual object tracking have significantly degraded the
performance of advanced trackers by introducing imperceptible perturbations
into images. These attack methods have garnered considerable attention from
researchers in recent years. However, there is still a lack of research on
designing adversarial defense methods specifically for visual object tracking.
To address these issues, we propose an effective additional pre-processing
network called DuaLossDef that eliminates adversarial perturbations during the
tracking process. DuaLossDef is deployed ahead of the search branche or
template branche of the tracker to apply defensive transformations to the input
images. Moreover, it can be seamlessly integrated with other visual trackers as
a plug-and-play module without requiring any parameter adjustments. We train
DuaLossDef using adversarial training, specifically employing Dua-Loss to
generate adversarial samples that simultaneously attack the classification and
regression branches of the tracker. Extensive experiments conducted on the
OTB100, LaSOT, and VOT2018 benchmarks demonstrate that DuaLossDef maintains
excellent defense robustness against adversarial attack methods in both
adaptive and non-adaptive attack scenarios. Moreover, when transferring the
defense network to other trackers, it exhibits reliable transferability.
Finally, DuaLossDef achieves a processing time of up to 5ms/frame, allowing
seamless integration with existing high-speed trackers without introducing
significant computational overhead. We will make our code publicly available
soon.