Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Fine-tuning for Backdoor Defense: Connecting Backdoor
  Attacks to Adversarial Attacks   Deep neural networks (DNNs) are known to be vulnerable to both backdoor
attacks as well as adversarial attacks. In the literature, these two types of
attacks are commonly treated as distinct problems and solved separately, since
they belong to training-time and inference-time attacks respectively. However,
in this paper we find an intriguing connection between them: for a model
planted with backdoors, we observe that its adversarial examples have similar
behaviors as its triggered samples, i.e., both activate the same subset of DNN
neurons. It indicates that planting a backdoor into a model will significantly
affect the model's adversarial examples. Based on this observations, we design
a new Adversarial Fine-Tuning (AFT) algorithm to defend against backdoor
attacks. We empirically show that, against 5 state-of-the-art backdoor attacks,
our AFT can effectively erase the backdoor triggers without obvious performance
degradation on clean samples and significantly outperforms existing defense
methods.

15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg