Alternatives and similar repositories for TRADES

Users that are interested in TRADES are comparing it to the libraries listed below

• locuslab / fast_adversarial
  [ICLR 2020] A repository for extremely fast adversarial training using FGSM
  ☆443Updated 9 months ago
• MadryLab / cifar10_challenge
  A challenge to explore adversarial robustness of neural networks on CIFAR10.
  ☆494Updated 3 years ago
• locuslab / smoothing
  Provable adversarial robustness at ImageNet scale
  ☆388Updated 5 years ago
• ylsung / pytorch-adversarial-training
  PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
  ☆250Updated 4 years ago
• mahyarnajibi / FreeAdversarialTraining
  PyTorch Implementation of Adversarial Training for Free!
  ☆246Updated 3 years ago
• fra31 / auto-attack
  Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
  ☆695Updated last year
• RobustBench / robustbench
  RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]
  ☆706Updated last month
• P2333 / Bag-of-Tricks-for-AT
  Empirical tricks for training robust models (ICLR 2021)
  ☆253Updated last year
• P2333 / Papers-of-Robust-ML
  Related papers for robust machine learning
  ☆568Updated last year
• chawins / Adversarial-Examples-Reading-List
  This is the reading list mainly on adversarial examples (attacks, defenses, etc.) I try to keep and update regularly.
  ☆226Updated 5 years ago
• ndb796 / Pytorch-Adversarial-Training-CIFAR
  This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.
  ☆165Updated 4 years ago
• YisenWang / MART
  Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"
  ☆147Updated 4 years ago
• locuslab / robust_overfitting
  ☆157Updated 4 years ago
• cg563 / simple-blackbox-attack
  Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"
  ☆198Updated 2 years ago
• tao-bai / attack-and-defense-methods
  A curated list of papers on adversarial machine learning (adversarial examples and defense methods).
  ☆210Updated 2 years ago
• wanglouis49 / pytorch-adversarial_box
  PyTorch library for adversarial attack and training
  ☆145Updated 6 years ago
• MadryLab / mnist_challenge
  A challenge to explore adversarial robustness of neural networks on MNIST.
  ☆751Updated 3 years ago
• zjfheart / Friendly-Adversarial-Training
  Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)
  ☆125Updated last year
• max-andr / square-attack
  Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]
  ☆164Updated 4 years ago
• thu-ml / ares
  A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
  ☆505Updated last year
• Harry24k / PGD-pytorch
  A pytorch implementation of "Towards Deep Learning Models Resistant to Adversarial Attacks"
  ☆154Updated 5 years ago
• yaircarmon / semisup-adv
  Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf
  ☆141Updated 5 years ago
• huanzhang12 / ZOO-Attack
  ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks
  ☆169Updated 3 years ago
• MadryLab / robustness
  A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
  ☆935Updated last year
• utkuozbulak / pytorch-cnn-adversarial-attacks
  Pytorch implementation of convolutional neural network adversarial attack techniques
  ☆357Updated 6 years ago
• BorealisAI / advertorch
  A Toolbox for Adversarial Robustness Research
  ☆1,336Updated last year
• Hadisalman / smoothing-adversarial
  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
  ☆225Updated 5 years ago
• 1Konny / FGSM
  Simple pytorch implementation of FGSM and I-FGSM
  ☆288Updated 7 years ago
• lfz / Guided-Denoise
  The winning submission for NIPS 2017: Defense Against Adversarial Attack of team TSAIL
  ☆236Updated 7 years ago
• MadryLab / constructed-datasets
  Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"
  ☆187Updated 4 years ago