LaVAN: Localized and Visible Adversarial Noise
Abstract
Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without covering any of the main object(s) in the image. We show that it is possible to generate localized adversarial noises that cover only 2% of the pixels in the image, none of them over the main object, and that are transferable across images and locations, and successfully fool a state-of-the-art Inception v3 model with very high success rates.
Cite
Text
Karmon et al. "LaVAN: Localized and Visible Adversarial Noise." International Conference on Machine Learning, 2018.Markdown
[Karmon et al. "LaVAN: Localized and Visible Adversarial Noise." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/karmon2018icml-lavan/)BibTeX
@inproceedings{karmon2018icml-lavan,
title = {{LaVAN: Localized and Visible Adversarial Noise}},
author = {Karmon, Danny and Zoran, Daniel and Goldberg, Yoav},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {2507-2515},
volume = {80},
url = {https://mlanthology.org/icml/2018/karmon2018icml-lavan/}
}