| yaodongyu/TRADES |
459 |
|
0 |
0 |
about 3 years ago |
0 |
|
4 |
mit |
Python |
| TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization) |
| Mrzhouqifei/DBA |
210 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
|
Python |
| Detection by Attack: Detecting Adversarial Samples by Undercover Attack |
| gongzhitaao/tensorflow-adversarial |
204 |
|
0 |
0 |
over 7 years ago |
0 |
|
3 |
mit |
Python |
| Crafting adversarial images |
| ftramer/ensemble-adv-training |
100 |
|
0 |
0 |
almost 9 years ago |
0 |
|
1 |
mit |
Python |
| Ensemble Adversarial Training on MNIST |
| wanglouis49/pytorch-adversarial_box |
88 |
|
0 |
0 |
about 7 years ago |
0 |
|
0 |
|
Python |
| PyTorch library for adversarial attack and training |
| verazuo/badnets-pytorch |
70 |
|
0 |
0 |
over 3 years ago |
0 |
|
1 |
mit |
Python |
| Simple PyTorch implementations of Badnets on MNIST and CIFAR10. |
| greentfrapp/boundary-attack |
67 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
|
Python |
| Implementation of the Boundary Attack algorithm as described in Brendel, Wieland, Jonas Rauber, and Matthias Bethge. "Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models." arXiv preprint arXiv:1712.04248 (2017). |
| bethgelab/AnalysisBySynthesis |
57 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
apache-2.0 |
Python |
| Adversarially Robust Neural Network on MNIST. |
| rajatvd/NeuralODE |
38 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
mit |
Python |
| Experiments with Neural ODEs and Adversarial Attacks |
| henry8527/GCE |
31 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
|
Python |
| [ICCV'19] Improving Adversarial Robustness via Guided Complement Entropy |
