Dec 10, 2018 � In this work, we show that adversarial training is more effective in preventing universal perturbations, where the same perturbation needs to fool a classifier�...
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
In this work, we show that adversarial training is more effective in preventing universal perturbations, where the same perturbation needs to fool a classifier�...
sPGD attacks [5, 6] were developed to produce image-agnostic attacks that are resilient to defending models through adversarial training. Recently, FTGAP [7]�...
This repository contains the Tensorflow implementation of Defense against Universal Adversarial Perturbations (CVPR2018)
The PRN also generalizes well in the sense that training for one targeted network defends another net- work with a comparable success rate. 1. Introduction.
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May 16, 2020 � Defending against Universal Perturbations with Shared Adversarial Training [35]. This paper introduces the idea of 'shared adversarial.
This paper proposes a novel deep learning technique for generating more transferable universal adversarial perturbations (UAPs) and proposes a loss�...
To defend against these perturbations, we propose univer- sal adversarial training, which models the problem of robust classifier generation as a two-player min�...