  Adversarial attacks in visual object tracking have significantly degraded the
performance of advanced trackers by introducing imperceptible perturbations
into images. However, there is still a lack of research on designing
adversarial defense methods for object tracking. To address these issues, we
propose an effective auxiliary pre-processing defense network, AADN, which
performs defensive transformations on the input images before feeding them into
the tracker. Moreover, it can be seamlessly integrated with other visual
trackers as a plug-and-play module without parameter adjustments. We train AADN
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 AADN maintains excellent
defense robustness against adversarial attack methods in both adaptive and
non-adaptive attack scenarios. Moreover, when transferring the defense network
to heterogeneous trackers, it exhibits reliable transferability. Finally, AADN
achieves a processing time of up to 5ms/frame, allowing seamless integration
with existing high-speed trackers without introducing significant computational
overhead.
