cfinlay / tulip
Scaleable input gradient regularization
☆22Updated 5 years ago
Alternatives and similar repositories for tulip:
Users that are interested in tulip are comparing it to the libraries listed below
- ☆87Updated 7 months ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 4 years ago
- Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]☆18Updated 6 years ago
- ☆31Updated 4 years ago
- [ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation☆54Updated last week
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 4 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- [CVPR'19] Trust Region Based Adversarial Attack☆20Updated 4 years ago
- ☆19Updated 5 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- An (imperfect) implementation of wide resnets and Parseval regularization☆9Updated 4 years ago
- ☆20Updated 7 months ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16Updated 5 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆34Updated 6 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 5 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆63Updated 5 years ago
- A method based on manifold regularization for training adversarially robust neural networks☆9Updated 5 years ago
- Certifying Some Distributional Robustness with Principled Adversarial Training (https://arxiv.org/abs/1710.10571)☆45Updated 6 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆34Updated 4 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 7 months ago
- ☆13Updated 6 years ago
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 6 years ago
- Code for Stability Training with Noise (STN)☆21Updated 4 years ago
- Geometric Certifications of Neural Nets☆41Updated 2 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- Analysis of Adversarial Logit Pairing☆60Updated 6 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"☆50Updated 3 years ago
- Coupling rejection strategy against adversarial attacks (CVPR 2022)☆28Updated 3 years ago