Alternatives and similar repositories for adversarial-nonparametrics

Users that are interested in adversarial-nonparametrics are comparing it to the libraries listed below

• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆32Updated 5 years ago
• MadryLab / robust-features-code
  Code for "Robustness May Be at Odds with Accuracy"
  ☆91Updated 2 years ago
• tcwangshiqi-columbia / Interval-Attack
  Interval attacks (adversarial ML)
  ☆21Updated 6 years ago
• Hadisalman / robust-verify-benchmark
  Benchmark for LP-relaxed robustness verification of ReLU-networks
  ☆42Updated 6 years ago
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 2 years ago
• locuslab / projected_sinkhorn
  ☆88Updated last year
• microsoft / denoised-smoothing
  Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
  ☆96Updated 4 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 7 years ago
• ftramer / Excessive-Invariance
  Source code for the paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness"
  ☆25Updated 5 years ago
• guanghelee / neurips19-certificates-of-robustness
  "Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers" (NeurIPS 2019, previously called "A Stratified Approach …
  ☆17Updated 5 years ago
• Bai-Li / STN-Code
  Code for Stability Training with Noise (STN)
  ☆22Updated 4 years ago
• huanzhang12 / CROWN-IBP
  Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTor…
  ☆98Updated 4 years ago
• jonasrauber / linear-region-attack
  A powerful white-box adversarial attack that exploits knowledge about the geometry of neural networks to find minimal adversarial perturb…
  ☆12Updated 5 years ago
• locuslab / robust_union
  [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
  ☆26Updated last year
• uclaml / RayS
  RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)
  ☆56Updated 4 years ago
• ZhaoyangLyu / POPQORN
  An Algorithm to Quantify Robustness of Recurrent Neural Networks
  ☆49Updated 5 years ago
• adversarial-robustness-benchmark / adversarial-robustness-benchmark
  ☆18Updated 5 years ago
• Hadisalman / smoothing-adversarial
  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
  ☆226Updated 5 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
• vikramnitin9 / adversarial-learning-literature
  ☆37Updated 5 years ago
• tonyduan / rs4a
  Randomized Smoothing of All Shapes and Sizes (ICML 2020).
  ☆51Updated 5 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 6 years ago
• MadryLab / relu_stable
  ☆26Updated 2 years ago
• max-andr / cross-lipschitz
  Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]
  ☆18Updated 7 years ago
• bethgelab / AnalysisBySynthesis
  Adversarially Robust Neural Network on MNIST.
  ☆63Updated 3 years ago
• MadryLab / constructed-datasets
  Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"
  ☆186Updated 4 years ago
• google-deepmind / interval-bound-propagation
  This repository contains a simple implementation of Interval Bound Propagation (IBP) using TensorFlow: https://arxiv.org/abs/1810.12715
  ☆161Updated 5 years ago
• Cold-Winter / Nattack
  ☆48Updated 4 years ago
• robust-ml / robust-ml.github.io
  A community-run reference for state-of-the-art adversarial example defenses.
  ☆50Updated 11 months ago
• robust-ml / robustml
  Interfaces for defining Robust ML models and precisely specifying the threat models under which they claim to be secure.
  ☆62Updated 6 years ago