Alternatives and similar repositories for constructed-datasets

Users that are interested in constructed-datasets are comparing it to the libraries listed below

• MadryLab / robust_representations
  Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
  ☆164Updated 5 years ago
• MadryLab / robust-features-code
  Code for "Robustness May Be at Odds with Accuracy"
  ☆91Updated 2 years ago
• Hadisalman / smoothing-adversarial
  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
  ☆227Updated 6 years ago
• evaluating-adversarial-robustness / adv-eval-paper
  LaTeX source for the paper "On Evaluating Adversarial Robustness"
  ☆259Updated 4 years ago
• ashafahi / free_adv_train
  Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…
  ☆177Updated last year
• jeromerony / fast_adversarial
  Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"
  ☆137Updated 5 years ago
• rakutentech / stAdv
  Spatially Transformed Adversarial Examples with TensorFlow
  ☆75Updated 7 years ago
• yaircarmon / semisup-adv
  Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf
  ☆141Updated 5 years ago
• aamir-mustafa / pcl-adversarial-defense
  Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019
  ☆58Updated 6 years ago
• revbucket / mister_ed
  Pytorch Adversarial Attack Framework
  ☆78Updated 6 years ago
• locuslab / projected_sinkhorn
  ☆88Updated last year
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 3 years ago
• s-huu / TurningWeaknessIntoStrength
  Official implementation for paper: A New Defense Against Adversarial Images: Turning a Weakness into a Strength
  ☆38Updated 5 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 7 years ago
• wanglouis49 / pytorch-adversarial_box
  PyTorch library for adversarial attack and training
  ☆145Updated 7 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 6 years ago
• locuslab / smoothing
  Provable adversarial robustness at ImageNet scale
  ☆403Updated 6 years ago
• kabkabm / defensegan
  Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)
  ☆246Updated 6 years ago
• Haichao-Zhang / FeatureScatter
  Feature Scattering Adversarial Training (NeurIPS19)
  ☆74Updated last year
• mahyarnajibi / FreeAdversarialTraining
  PyTorch Implementation of Adversarial Training for Free!
  ☆248Updated 4 years ago
• huanzhang12 / ZOO-Attack
  ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks
  ☆170Updated 4 years ago
• cg563 / simple-blackbox-attack
  Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"
  ☆200Updated 2 years ago
• YyzHarry / ME-Net
  [ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
  ☆54Updated 2 months 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.
  ☆228Updated 6 years ago
• thong3le / blackbox-attack
  Blackbox attacks for deep neural network models
  ☆70Updated 7 years ago
• ermongroup / generative_adversary
  Code for the unrestricted adversarial examples paper (NeurIPS 2018)
  ☆65Updated 6 years ago
• a1600012888 / YOPO-You-Only-Propagate-Once
  Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle
  ☆178Updated last year
• uclaml / RayS
  RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)
  ☆56Updated 5 years ago
• locuslab / robust_overfitting
  ☆162Updated 4 years ago
• huanzhang12 / CLEVER
  CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks
  ☆63Updated 4 years ago