Alternatives and similar repositories for Papers-of-Robust-ML:

Users that are interested in Papers-of-Robust-ML are comparing it to the libraries listed below

• yaodongyu / TRADES
  TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)
  ☆526Updated last year
• RobustBench / robustbench
  RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]
  ☆689Updated 3 weeks ago
• locuslab / fast_adversarial
  [ICLR 2020] A repository for extremely fast adversarial training using FGSM
  ☆439Updated 5 months ago
• locuslab / smoothing
  Provable adversarial robustness at ImageNet scale
  ☆379Updated 5 years ago
• MadryLab / robustness
  A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
  ☆923Updated last year
• MadryLab / cifar10_challenge
  A challenge to explore adversarial robustness of neural networks on CIFAR10.
  ☆490Updated 3 years ago
• ylsung / pytorch-adversarial-training
  PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
  ☆246Updated 4 years ago
• fra31 / auto-attack
  Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
  ☆678Updated 8 months ago
• mahyarnajibi / FreeAdversarialTraining
  PyTorch Implementation of Adversarial Training for Free!
  ☆242Updated 3 years ago
• P2333 / Bag-of-Tricks-for-AT
  Empirical tricks for training robust models (ICLR 2021)
  ☆252Updated last year
• thu-ml / ares
  A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
  ☆494Updated last year
• tao-bai / attack-and-defense-methods
  A curated list of papers on adversarial machine learning (adversarial examples and defense methods).
  ☆211Updated 2 years ago
• chawins / Adversarial-Examples-Reading-List
  This is the reading list mainly on adversarial examples (attacks, defenses, etc.) I try to keep and update regularly.
  ☆222Updated 5 years ago
• MadryLab / mnist_challenge
  A challenge to explore adversarial robustness of neural networks on MNIST.
  ☆737Updated 2 years ago
• chbrian / awesome-adversarial-examples-dl
  A curated list of awesome resources for adversarial examples in deep learning
  ☆262Updated 3 years ago
• BorealisAI / advertorch
  A Toolbox for Adversarial Robustness Research
  ☆1,317Updated last year
• evaluating-adversarial-robustness / adv-eval-paper
  LaTeX source for the paper "On Evaluating Adversarial Robustness"
  ☆253Updated 3 years ago
• 1Konny / FGSM
  Simple pytorch implementation of FGSM and I-FGSM
  ☆281Updated 6 years ago
• sarathknv / adversarial-examples-pytorch
  Implementation of Papers on Adversarial Examples
  ☆391Updated last year
• cg563 / simple-blackbox-attack
  Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"
  ☆196Updated last year
• ndb796 / Pytorch-Adversarial-Training-CIFAR
  This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.
  ☆162Updated 3 years ago
• locuslab / robust_overfitting
  ☆155Updated 3 years ago
• hendrycks / robustness
  Corruption and Perturbation Robustness (ICLR 2019)
  ☆1,039Updated 2 years ago
• MadryLab / constructed-datasets
  Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"
  ☆184Updated 4 years ago
• jeromerony / adversarial-library
  Library containing PyTorch implementations of various adversarial attacks and resources
  ☆149Updated 3 weeks ago
• YisenWang / MART
  Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"
  ☆144Updated 4 years ago
• Hadisalman / smoothing-adversarial
  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
  ☆224Updated 5 years ago
• zjfheart / Friendly-Adversarial-Training
  Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)
  ☆124Updated last year
• locuslab / convex_adversarial
  A method for training neural networks that are provably robust to adversarial attacks.
  ☆381Updated 2 years ago