oscarknagg / adversarial
Creating and defending against adversarial examples
☆42Updated 5 years ago
Related projects: ⓘ
- Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"☆143Updated 3 years ago
- Code for "On Adaptive Attacks to Adversarial Example Defenses"☆84Updated 3 years ago
- This repository contains implementation of 4 adversarial attacks : FGSM, Basic Iterative Method, Projected Gradient Descent(Madry's Attac…☆31Updated 5 years ago
- Code for ICML2019 Paper "On the Convergence and Robustness of Adversarial Training"☆33Updated 4 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆54Updated 2 years ago
- Craft poisoned data using MetaPoison☆47Updated 3 years ago
- Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".☆121Updated 3 years ago
- ☆9Updated 3 years ago
- CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks☆60Updated 3 years ago
- Repository for Certified Defenses for Adversarial Patch ICLR-2020☆32Updated 4 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆136Updated 4 years ago
- PyTorch implementations of Adversarial defenses and utils.☆34Updated 8 months ago
- Adversarial Distributional Training (NeurIPS 2020)☆61Updated 3 years ago
- ☆53Updated last year
- Code for the unrestricted adversarial examples paper (NeurIPS 2018)☆63Updated 5 years ago
- ☆23Updated 2 years ago
- Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)☆124Updated last year
- A unified benchmark problem for data poisoning attacks☆148Updated 11 months ago
- Blackbox attacks for deep neural network models☆72Updated 6 years ago
- ATTA (Efficient Adversarial Training with Transferable Adversarial Examples)☆32Updated 4 years ago
- This repo keeps track of popular provable training and verification approaches towards robust neural networks, including leaderboards on …☆99Updated last year
- ☆153Updated 3 years ago
- Understanding Catastrophic Overfitting in Single-step Adversarial Training [AAAI 2021]☆27Updated 2 years ago
- Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)☆108Updated 6 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆94Updated 2 years ago
- Universal Adversarial Perturbations (UAPs) for PyTorch☆45Updated 3 years ago
- Implementation for "Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks"☆13Updated 3 years ago
- RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)☆54Updated 3 years ago
- A pytorch implementation of "Towards Evaluating the Robustness of Neural Networks"☆52Updated 5 years ago
- Code for "Diversity can be Transferred: Output Diversification for White- and Black-box Attacks"☆52Updated 3 years ago