Alternatives and similar repositories for fooling

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

• dtak / adversarial-robustness-public
  Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…
  ☆55Updated 2 years ago
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 6 years ago
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 2 years ago
• Bai-Li / STN-Code
  Code for Stability Training with Noise (STN)
  ☆22Updated 4 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆31Updated 5 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 7 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 6 years ago
• sunblaze-ucb / decision-boundaries
  code we used in Decision Boundary Analysis of Adversarial Examples https://openreview.net/forum?id=BkpiPMbA-
  ☆29Updated 7 years ago
• max-andr / cross-lipschitz
  Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]
  ☆18Updated 7 years ago
• cfinlay / tulip
  Scaleable input gradient regularization
  ☆22Updated 6 years ago
• tcwangshiqi-columbia / Interval-Attack
  Interval attacks (adversarial ML)
  ☆21Updated 6 years ago
• adversarial-robustness-benchmark / adversarial-robustness-benchmark
  ☆18Updated 6 years ago
• uds-lsv / evaluating-logit-pairing-methods
  Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]
  ☆19Updated 6 years ago
• amirgholami / TRAttack
  [CVPR'19] Trust Region Based Adversarial Attack
  ☆20Updated 4 years ago
• locuslab / projected_sinkhorn
  ☆88Updated last year
• yk / icml19_public
  ☆22Updated 5 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 6 years ago
• xiahaifeng1995 / adversarial-robustness
  ☆26Updated 6 years ago
• ShawnXYang / AT_HE
  ☆35Updated 4 years ago
• cetmann / robustness-interpretability
  Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …
  ☆16Updated 6 years ago
• ZiangYan / deepdefense.pytorch
  Implementation of our NeurIPS 2018 paper: Deep Defense: Training DNNs with Improved Adversarial Robustness
  ☆39Updated 6 years ago
• aamir-mustafa / super-resolution-adversarial-defense
  Image Super-Resolution as a Defense Against Adversarial Attacks
  ☆89Updated 6 years ago
• columbia / Metric_Learning_Adversarial_Robustness
  Code for NeurIPS 2019 Paper
  ☆48Updated 5 years ago
• blackbox-bandits / blackbox-attacks-bandits-priors
  Code for "Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors"
  ☆14Updated 7 years ago
• Cold-Winter / Nattack
  ☆48Updated 4 years ago
• P2333 / Adaptive-Diversity-Promoting
  Adversarial Defense for Ensemble Models (ICML 2019)
  ☆61Updated 4 years ago
• revbucket / mister_ed
  Pytorch Adversarial Attack Framework
  ☆78Updated 6 years ago
• tonyduan / rs4a
  Randomized Smoothing of All Shapes and Sizes (ICML 2020).
  ☆51Updated 5 years ago
• microsoft / denoised-smoothing
  Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
  ☆97Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago