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Adversarial Attack

60 papers with code · Adversarial

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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

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

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

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

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

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

CVPR 2018 lfz/Guided-Denoise

First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE IMAGE CLASSIFICATION

On Evaluating Adversarial Robustness

18 Feb 2019evaluating-adversarial-robustness/adv-eval-paper

Correctly evaluating defenses against adversarial examples has proven to be extremely difficult.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE

Boosting Adversarial Attacks with Momentum

CVPR 2018 dongyp13/Non-Targeted-Adversarial-Attacks

To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.

ADVERSARIAL ATTACK

Generating Natural Adversarial Examples

ICLR 2018 zhengliz/natural-adversary

Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.

ADVERSARIAL ATTACK IMAGE CLASSIFICATION MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE