While deep convolutional neural networks (CNNs) are vulnerable to adversarial
attacks, considerably few efforts have been paid to construct robust deep
tracking algorithms against adversarial attacks. Current studies on adversarial
attack and defense mainly reside in a single image. In this work, we first
attempt to generate adversarial examples on top of video sequences to improve
the tracking robustness against adversarial attacks. To this end, we take
temporal motion into consideration when generating lightweight perturbations
over the estimated tracking results frame-by-frame. On one hand, we add the
temporal perturbations into the original video sequences as adversarial
examples to greatly degrade the tracking performance. On the other hand, we
sequentially estimate the perturbations from input sequences and learn to
eliminate their effect for performance restoration. We apply the proposed
adversarial attack and defense approaches to state-of-the-art deep tracking
algorithms. Extensive evaluations on the benchmark datasets demonstrate that
our defense method not only eliminates the large performance drops caused by
adversarial attacks, but also achieves additional performance gains when deep
trackers are not under adversarial attacks.

本文提出了一种基于视频序列的对抗攻击和防御方法，通过考虑时间运动并生成轻量级扰动，有效提高了深度跟踪算法的鲁棒性，结果表明该方法不仅能消除对抗攻击带来的大幅性能下降，同时在未受到攻击时也能获得额外的性能增益。