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Adversarial Attack

131 papers with code · Adversarial

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

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Greatest papers with code

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

3 Oct 2016openai/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

Foolbox: A Python toolbox to benchmark the robustness of machine learning models

13 Jul 2017bethgelab/foolbox

Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models.

ADVERSARIAL ATTACK

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

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

29 Apr 2020QData/TextAttack

TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.

ADVERSARIAL ATTACK ADVERSARIAL TEXT ADVERSARIAL TRAINING DATA AUGMENTATION LEXICAL ENTAILMENT MACHINE TRANSLATION TEXT CLASSIFICATION

Towards Evaluating the Robustness of Neural Networks

16 Aug 2016carlini/nn_robust_attacks

Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from $95\%$ to $0. 5\%$.

ADVERSARIAL ATTACK

Natural Adversarial Examples

16 Jul 2019hendrycks/natural-adv-examples

We curate 7, 500 natural adversarial examples and release them in an ImageNet classifier test set that we call ImageNet-A.

ADVERSARIAL ATTACK DOMAIN GENERALIZATION

Provable defenses against adversarial examples via the convex outer adversarial polytope

ICML 2018 locuslab/convex_adversarial

We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations on the training data.

ADVERSARIAL ATTACK