Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Walking on the Edge: Fast, Low-Distortion Adversarial Examples   Adversarial examples of deep neural networks are receiving ever increasing
attention because they help in understanding and reducing the sensitivity to
their input. This is natural given the increasing applications of deep neural
networks in our everyday lives. When white-box attacks are almost always
successful, it is typically only the distortion of the perturbations that
matters in their evaluation.
  In this work, we argue that speed is important as well, especially when
considering that fast attacks are required by adversarial training. Given more
time, iterative methods can always find better solutions. We investigate this
speed-distortion trade-off in some depth and introduce a new attack called
boundary projection (BP) that improves upon existing methods by a large margin.
Our key idea is that the classification boundary is a manifold in the image
space: we therefore quickly reach the boundary and then optimize distortion on
this manifold.

6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn