Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robustness of 3D Deep Learning in an Adversarial Setting   Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.

27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th