Adversarial Defense
92 papers with code • 9 benchmarks • 6 datasets
Competitions with currently unpublished results:
Benchmarks
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CIFAR-10
ImageNet
MNIST
CIFAR-100
miniImageNet
Icons-50
Greatest papers with code
Towards Deep Learning Models Resistant to Adversarial Attacks
tensorflow/cleverhans
•
•
ICLR 2018
Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
Ranked #2 on
Robust classification
on CIFAR-10
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
The Limitations of Deep Learning in Adversarial Settings
In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
advertorch is a toolbox for adversarial robustness research.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
anishathalye/obfuscated-gradients
•
•
ICML 2018
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.
Feature Denoising for Improving Adversarial Robustness
facebookresearch/ImageNet-Adversarial-Training
•
•
CVPR 2019
This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
hendrycks/robustness
•
•
ICLR 2019
Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
Ranked #10 on
Domain Generalization
on ImageNet-C
Countering Adversarial Images using Input Transformations
facebookresearch/adversarial_image_defenses
•
•
ICLR 2018
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.
Theoretically Principled Trade-off between Robustness and Accuracy
yaodongyu/TRADES
•
•
We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples.
Ranked #3 on
Adversarial Attack
on CIFAR-10