Adversarial Defense

92 papers with code • 9 benchmarks • 6 datasets

Competitions with currently unpublished results:

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.

Adversarial Attack Adversarial Defense +2

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

cleverhans-lab/cleverhans 3 Oct 2016

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Adversarial Attack Adversarial Defense

The Limitations of Deep Learning in Adversarial Settings

tensorflow/cleverhans 24 Nov 2015

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 Attack Adversarial Defense

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

stellargraph/stellargraph 5 Mar 2019

Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.

Adversarial Attack Adversarial Defense

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.

Adversarial Attack Adversarial Defense

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.

Adversarial Defense Domain Generalization

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.

Adversarial Defense General Classification +1

Theoretically Principled Trade-off between Robustness and Accuracy

yaodongyu/TRADES 24 Jan 2019

We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples.

Adversarial Attack Adversarial Defense +1