Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE
Abstract
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, unrestricted adversarial attack has raised great concern and presented a new threat to the AI safety. In this paper, we propose a wavelet-VAE structure to reconstruct an input image and generate adversarial examples by modifying the latent code. Different from perturbation-based attack, the modifications of the proposed method are not limited but imperceptible to human eyes. Experiments show that our method can generate high quality adversarial examples on ImageNet dataset.
Cite
Text
Xiang et al. "Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE." ICML 2021 Workshops: AML, 2021.Markdown
[Xiang et al. "Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE." ICML 2021 Workshops: AML, 2021.](https://mlanthology.org/icmlw/2021/xiang2021icmlw-improving/)BibTeX
@inproceedings{xiang2021icmlw-improving,
title = {{Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE}},
author = {Xiang, Wenzhao and Liu, Chang and Zheng, Shibao},
booktitle = {ICML 2021 Workshops: AML},
year = {2021},
url = {https://mlanthology.org/icmlw/2021/xiang2021icmlw-improving/}
}