Frequency Domain Model Augmentation for Adversarial Attack
- URL: http://arxiv.org/abs/2207.05382v1
- Date: Tue, 12 Jul 2022 08:26:21 GMT
- Title: Frequency Domain Model Augmentation for Adversarial Attack
- Authors: Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xianglong Liu,
Jian Zhang, Jingkuan Song
- Abstract summary: For black-box attacks, the gap between the substitute model and the victim model is usually large.
We propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models.
- Score: 91.36850162147678
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For black-box attacks, the gap between the substitute model and the victim
model is usually large, which manifests as a weak attack performance. Motivated
by the observation that the transferability of adversarial examples can be
improved by attacking diverse models simultaneously, model augmentation methods
which simulate different models by using transformed images are proposed.
However, existing transformations for spatial domain do not translate to
significantly diverse augmented models. To tackle this issue, we propose a
novel spectrum simulation attack to craft more transferable adversarial
examples against both normally trained and defense models. Specifically, we
apply a spectrum transformation to the input and thus perform the model
augmentation in the frequency domain. We theoretically prove that the
transformation derived from frequency domain leads to a diverse spectrum
saliency map, an indicator we proposed to reflect the diversity of substitute
models. Notably, our method can be generally combined with existing attacks.
Extensive experiments on the ImageNet dataset demonstrate the effectiveness of
our method, \textit{e.g.}, attacking nine state-of-the-art defense models with
an average success rate of \textbf{95.4\%}. Our code is available in
\url{https://github.com/yuyang-long/SSA}.
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