Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Tracking against Adversarial Attacks   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.

19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ