Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Adversarial Attacks on Neural Networks with Better Optimizer   Convolutional neural networks have outperformed humans in image recognition
tasks, but they remain vulnerable to attacks from adversarial examples. Since
these data are crafted by adding imperceptible noise to normal images, their
existence poses potential security threats to deep learning systems.
Sophisticated adversarial examples with strong attack performance can also be
used as a tool to evaluate the robustness of a model. However, the success rate
of adversarial attacks can be further improved in black-box environments.
Therefore, this study combines a modified Adam gradient descent algorithm with
the iterative gradient-based attack method. The proposed Adam Iterative Fast
Gradient Method is then used to improve the transferability of adversarial
examples. Extensive experiments on ImageNet showed that the proposed method
offers a higher attack success rate than existing iterative methods. By
extending our method, we achieved a state-of-the-art attack success rate of
95.0% on defense models.

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The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex