locuslab / smoothingLinks
Provable adversarial robustness at ImageNet scale
☆390Updated 6 years ago
Alternatives and similar repositories for smoothing
Users that are interested in smoothing are comparing it to the libraries listed below
Sorting:
- [ICLR 2020] A repository for extremely fast adversarial training using FGSM☆443Updated 10 months ago
- TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)☆537Updated 2 years ago
- A challenge to explore adversarial robustness of neural networks on CIFAR10.☆496Updated 3 years ago
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆225Updated 5 years ago
- LaTeX source for the paper "On Evaluating Adversarial Robustness"☆255Updated 4 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 4 years ago
- ☆157Updated 4 years ago
- Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".☆123Updated 4 years ago
- PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.☆251Updated 4 years ago
- A method for training neural networks that are provably robust to adversarial attacks.☆387Updated 3 years ago
- Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"☆700Updated last year
- Related papers for robust machine learning☆568Updated 2 years ago
- Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"☆198Updated 2 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆142Updated 5 years ago
- Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"☆147Updated 4 years ago
- This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.☆167Updated 4 years ago
- Blackbox attacks for deep neural network models☆70Updated 6 years ago
- Empirical tricks for training robust models (ICLR 2021)☆253Updated 2 years ago
- A curated list of papers on adversarial machine learning (adversarial examples and defense methods).☆210Updated 3 years ago
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks☆169Updated 3 years ago
- PyTorch Implementation of Adversarial Training for Free!☆246Updated 3 years ago
- This repo keeps track of popular provable training and verification approaches towards robust neural networks, including leaderboards on …☆99Updated 2 years ago
- RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]☆713Updated 2 months ago
- Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]☆164Updated 4 years ago
- A unified benchmark problem for data poisoning attacks☆155Updated last year
- Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTor…☆95Updated 3 years ago
- Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)☆241Updated 5 years ago
- Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)☆125Updated last year
- A rich-documented PyTorch implementation of Carlini-Wagner's L2 attack.☆60Updated 6 years ago
- A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.☆937Updated last year