  Motion blur caused by the moving of the object or camera during the exposure
can be a key challenge for visual object tracking, affecting tracking accuracy
significantly. In this work, we explore the robustness of visual object
trackers against motion blur from a new angle, i.e., adversarial blur attack
(ABA). Our main objective is to online transfer input frames to their natural
motion-blurred counterparts while misleading the state-of-the-art trackers
during the tracking process. To this end, we first design the motion blur
synthesizing method for visual tracking based on the generation principle of
motion blur, considering the motion information and the light accumulation
process. With this synthetic method, we propose optimization-based ABA (OP-ABA)
by iteratively optimizing an adversarial objective function against the
tracking w.r.t. the motion and light accumulation parameters. The OP-ABA is
able to produce natural adversarial examples but the iteration can cause heavy
time cost, making it unsuitable for attacking real-time trackers. To alleviate
this issue, we further propose one-step ABA (OS-ABA) where we design and train
a joint adversarial motion and accumulation predictive network (JAMANet) with
the guidance of OP-ABA, which is able to efficiently estimate the adversarial
motion and accumulation parameters in a one-step way. The experiments on four
popular datasets (e.g., OTB100, VOT2018, UAV123, and LaSOT) demonstrate that
our methods are able to cause significant accuracy drops on four
state-of-the-art trackers with high transferability. Please find the source
code at \url{https://github.com/tsingqguo/ABA}.
