Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Layers Susceptible to Adversarial Attacks   In this paper, we investigate the use of pretraining with adversarial
networks, with the objective of discovering the relationship between network
depth and robustness. For this purpose, we selectively retrain different
portions of VGG and ResNet architectures on CIFAR-10, Imagenette, and ImageNet
using non-adversarial and adversarial data. Experimental results show that
susceptibility to adversarial samples is associated with low-level feature
extraction layers. Therefore, retraining of high-level layers is insufficient
for achieving robustness. Furthermore, adversarial attacks yield outputs from
early layers that differ statistically from features for non-adversarial
samples and do not permit consistent classification by subsequent layers. This
supports common hypotheses regarding the association of robustness with the
feature extractor, insufficiency of deeper layers in providing robustness, and
large differences in adversarial and non-adversarial feature vectors.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga