BibTex format
@inproceedings{Co:2019,
author = {Co, KT and Munoz, Gonzalez L and Lupu, E},
title = {Sensitivity of Deep Convolutional Networks to Gabor Noise},
url = {http://hdl.handle.net/10044/1/73383},
year = {2019}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually similar procedural noise patterns also act as UAPs. In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns. This behaviour, its causes, and implications deserve further in-depth study.
AU - Co,KT
AU - Munoz,Gonzalez L
AU - Lupu,E
PY - 2019///
TI - Sensitivity of Deep Convolutional Networks to Gabor Noise
UR - http://hdl.handle.net/10044/1/73383
ER -