Hadisalman / smoothing-adversarialLinks
Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
☆227Updated 6 years ago
Alternatives and similar repositories for smoothing-adversarial
Users that are interested in smoothing-adversarial are comparing it to the libraries listed below
Sorting:
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 5 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆141Updated 5 years ago
- ☆88Updated last year
- Provable adversarial robustness at ImageNet scale☆403Updated 6 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆100Updated 4 years ago
- Randomized Smoothing of All Shapes and Sizes (ICML 2020).☆51Updated 5 years ago
- LaTeX source for the paper "On Evaluating Adversarial Robustness"☆259Updated 4 years ago
- ☆162Updated 4 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆164Updated 5 years ago
- Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…☆177Updated last year
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Updated last year
- A method for training neural networks that are provably robust to adversarial attacks.☆390Updated 3 years ago
- Official implementation for paper: A New Defense Against Adversarial Images: Turning a Weakness into a Strength☆38Updated 5 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 5 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆74Updated last year
- Pytorch Adversarial Attack Framework☆78Updated 6 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 6 years ago
- CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks☆63Updated 4 years ago
- Interfaces for defining Robust ML models and precisely specifying the threat models under which they claim to be secure.☆62Updated 6 years ago
- Code for the unrestricted adversarial examples paper (NeurIPS 2018)☆65Updated 6 years ago
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks☆170Updated 4 years ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆129Updated 6 years ago
- RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)☆56Updated 5 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 3 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 6 years ago
- [ICLR 2020] A repository for extremely fast adversarial training using FGSM☆450Updated last year
- Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".☆125Updated 5 years ago
- [ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation☆54Updated 2 months ago
- Spatially Transformed Adversarial Examples with TensorFlow☆75Updated 7 years ago