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Greatest papers with code

Towards Deep Learning Models Resistant to Adversarial Attacks

ICLR 2018 tensorflow/cleverhans

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 DEFENSE ROBUST CLASSIFICATION

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

3 Oct 2016cleverhans-lab/cleverhans

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

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE

The Limitations of Deep Learning in Adversarial Settings

24 Nov 2015tensorflow/cleverhans

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

5 Mar 2019stellargraph/stellargraph

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

ICML 2018 anishathalye/obfuscated-gradients

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

ICLR 2019 hendrycks/robustness

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

ICLR 2018 facebookresearch/adversarial_image_defenses

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 IMAGE CLASSIFICATION

Theoretically Principled Trade-off between Robustness and Accuracy

24 Jan 2019yaodongyu/TRADES

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