Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Shuffling for Defending Deep Action Recognition Models against
  Adversarial Attacks   Recently, video-based action recognition methods using convolutional neural
networks (CNNs) achieve remarkable recognition performance. However, there is
still lack of understanding about the generalization mechanism of action
recognition models. In this paper, we suggest that action recognition models
rely on the motion information less than expected, and thus they are robust to
randomization of frame orders. Based on this observation, we develop a novel
defense method using temporal shuffling of input videos against adversarial
attacks for action recognition models. Another observation enabling our defense
method is that adversarial perturbations on videos are sensitive to temporal
destruction. To the best of our knowledge, this is the first attempt to design
a defense method specific to video-based action recognition models.

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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int