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Adversarial Attack

50 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

Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

1 Aug 2017QData/AdversarialDNN-Playground

Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE IMAGE CLASSIFICATION