BibTex format
@article{Co:2019,
author = {Co, KT and Muñoz-González, L and Lupu, EC},
title = {Sensitivity of Deep Convolutional Networks to Gabor Noise},
url = {http://arxiv.org/abs/1906.03455v2},
year = {2019}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - Deep Convolutional Networks (DCNs) have been shown to be sensitive toUniversal Adversarial Perturbations (UAPs): input-agnostic perturbations thatfool a model on large portions of a dataset. These UAPs exhibit interestingvisual patterns, but this phenomena is, as yet, poorly understood. Our workshows that visually similar procedural noise patterns also act as UAPs. Inparticular, we demonstrate that different DCN architectures are sensitive toGabor noise patterns. This behaviour, its causes, and implications deservefurther in-depth study.
AU - Co,KT
AU - Muñoz-González,L
AU - Lupu,EC
PY - 2019///
TI - Sensitivity of Deep Convolutional Networks to Gabor Noise
UR - http://arxiv.org/abs/1906.03455v2
ER -