Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhanced countering adversarial attacks via input denoising and feature
  restoring   Despite the fact that deep neural networks (DNNs) have achieved prominent
performance in various applications, it is well known that DNNs are vulnerable
to adversarial examples/samples (AEs) with imperceptible perturbations in
clean/original samples. To overcome the weakness of the existing defense
methods against adversarial attacks, which damages the information on the
original samples, leading to the decrease of the target classifier accuracy,
this paper presents an enhanced countering adversarial attack method IDFR (via
Input Denoising and Feature Restoring). The proposed IDFR is made up of an
enhanced input denoiser (ID) and a hidden lossy feature restorer (FR) based on
the convex hull optimization. Extensive experiments conducted on benchmark
datasets show that the proposed IDFR outperforms the various state-of-the-art
defense methods, and is highly effective for protecting target models against
various adversarial black-box or white-box attacks. \footnote{Souce code is
released at:
\href{https://github.com/ID-FR/IDFR}{https://github.com/ID-FR/IDFR}}

31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex