Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Defense: Training DNNs with Improved Adversarial Robustness   Despite the efficacy on a variety of computer vision tasks, deep neural
networks (DNNs) are vulnerable to adversarial attacks, limiting their
applications in security-critical systems. Recent works have shown the
possibility of generating imperceptibly perturbed image inputs (a.k.a.,
adversarial examples) to fool well-trained DNN classifiers into making
arbitrary predictions. To address this problem, we propose a training recipe
named "deep defense". Our core idea is to integrate an adversarial
perturbation-based regularizer into the classification objective, such that the
obtained models learn to resist potential attacks, directly and precisely. The
whole optimization problem is solved just like training a recursive network.
Experimental results demonstrate that our method outperforms training with
adversarial/Parseval regularizations by large margins on various datasets
(including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code
and models for reproducing our results are available at
https://github.com/ZiangYan/deepdefense.pytorch

6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom