locuslab / robust_unionLinks
[ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
☆26Updated 10 months ago
Alternatives and similar repositories for robust_union
Users that are interested in robust_union are comparing it to the libraries listed below
Sorting:
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 5 years ago
- Code for Stability Training with Noise (STN)☆22Updated 4 years ago
- Source code for the paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness"☆25Updated 5 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆19Updated 2 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 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
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 6 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆73Updated last year
- ☆13Updated 5 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 3 years ago
- Official implementation for paper: A New Defense Against Adversarial Images: Turning a Weakness into a Strength☆38Updated 5 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆23Updated 2 years ago
- ☆87Updated 10 months ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 4 years ago
- MACER: MAximizing CErtified Radius (ICLR 2020)☆30Updated 5 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- ☆55Updated 4 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- Analysis of Adversarial Logit Pairing☆60Updated 6 years ago
- [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…☆47Updated 3 years ago
- [NeurIPS 2021] Fast Certified Robust Training with Short Warmup☆24Updated 2 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 6 years ago
- Code for the paper "Adversarial Neural Pruning with Latent Vulnerability Suppression"☆15Updated 2 years ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆23Updated 6 years ago