BibTex format
@article{Co:2021,
author = {Co, KT and Muñoz-González, L and Kanthan, L and Lupu, EC},
title = {Real-time Detection of Practical Universal Adversarial Perturbations},
url = {http://arxiv.org/abs/2105.07334v2},
year = {2021}
}
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Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - Universal Adversarial Perturbations (UAPs) are a prominent class ofadversarial examples that exploit the systemic vulnerabilities and enablephysically realizable and robust attacks against Deep Neural Networks (DNNs).UAPs generalize across many different inputs; this leads to realistic andeffective attacks that can be applied at scale. In this paper we proposeHyperNeuron, an efficient and scalable algorithm that allows for the real-timedetection of UAPs by identifying suspicious neuron hyper-activations. Ourresults show the effectiveness of HyperNeuron on multiple tasks (imageclassification, object detection), against a wide variety of universal attacks,and in realistic scenarios, like perceptual ad-blocking and adversarialpatches. HyperNeuron is able to simultaneously detect both adversarial mask andpatch UAPs with comparable or better performance than existing UAP defenseswhilst introducing a significantly reduced latency of only 0.86 millisecondsper image. This suggests that many realistic and practical universal attackscan be reliably mitigated in real-time, which shows promise for the robustdeployment of machine learning systems.
AU - Co,KT
AU - Muñoz-González,L
AU - Kanthan,L
AU - Lupu,EC
PY - 2021///
TI - Real-time Detection of Practical Universal Adversarial Perturbations
UR - http://arxiv.org/abs/2105.07334v2
ER -