MadryLab / spatial-pytorch
Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).
☆26Updated 5 years ago
Related projects ⓘ
Alternatives and complementary repositories for spatial-pytorch
- ☆35Updated 3 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆44Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆99Updated 2 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 4 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆96Updated 3 years ago
- Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020☆24Updated 3 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆46Updated last year
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Implementation of our NeurIPS 2019 paper: Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks☆10Updated 4 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆25Updated 3 months ago
- Coupling rejection strategy against adversarial attacks (CVPR 2022)☆28Updated 2 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)☆21Updated 4 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆54Updated 2 years ago
- Code for "Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors"☆63Updated 4 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 4 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆64Updated 5 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 4 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- Code for NeurIPS 2019 Paper☆48Updated 4 years ago
- ☆87Updated 3 months ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆71Updated 5 months ago
- Source code for Learning Transferable Adversarial Examples via Ghost Networks (AAAI2020)☆59Updated 5 years ago
- Code for Stability Training with Noise (STN)☆21Updated 3 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 3 years ago
- Pytorch - Adversarial Training☆26Updated 6 years ago
- ☆46Updated 3 years ago