|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'503'724
Articles rated: 2609
23 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
AI-GAN: Attack-Inspired Generation of Adversarial Examples | | Tao Bai
; Jun Zhao
; Jinlin Zhu
; Shoudong Han
; Jiefeng Chen
; Bo Li
; | | Date: |
Thu, 6 Feb 2020 10:57:41 GMT (1831kb,D) | | Abstract: | Adversarial examples that can fool deep models are mainly crafted by adding
small perturbations imperceptible to human eyes. There are various
optimization-based methods in the literature to generate adversarial
perturbations, most of which are time-consuming. AdvGAN, a method proposed by
Xiao~emph{et al.}~in IJCAI~2018, employs Generative Adversarial Networks (GAN)
to generate adversarial perturbation with original images as inputs, which is
faster than optimization-based methods at inference time. AdvGAN, however,
fixes the target classes in the training and we find it difficult to train
AdvGAN when it is modified to take original images and target classes as
inputs. In this paper, we propose mbox{Attack-Inspired} GAN (mbox{AI-GAN})
with a different training strategy to solve this problem. mbox{AI-GAN} is a
two-stage method, in which we use projected gradient descent (PGD) attack to
inspire the training of GAN in the first stage and apply standard training of
GAN in the second stage.
Once trained, the Generator can approximate the conditional distribution of
adversarial instances and generate mbox{imperceptible} adversarial
perturbations given different target classes. We conduct experiments and
evaluate the performance of mbox{AI-GAN} on MNIST and mbox{CIFAR-10}.
Compared with AdvGAN, mbox{AI-GAN} achieves higher attack success rates with
similar perturbation magnitudes. | | Source: | arXiv, 2002.2196 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER