https://github.com/tantara/peernets-pytorch
A pytorch implementation of 'PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks'
https://github.com/tantara/peernets-pytorch
Last synced: about 1 month ago
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A pytorch implementation of 'PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks'
- Host: GitHub
- URL: https://github.com/tantara/peernets-pytorch
- Owner: tantara
- License: mit
- Created: 2018-10-14T14:55:29.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-15T13:04:40.000Z (over 6 years ago)
- Last Synced: 2025-04-15T08:52:19.145Z (about 1 month ago)
- Language: Python
- Homepage: https://medium.com/@tantara/adversarial-attack-part-1-a830ec92acde
- Size: 693 KB
- Stars: 14
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PeerNets on PyTorch 1.0
A pytorch implementation of [PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks](https://arxiv.org/abs/1806.00088)
**Keywords**
Adversarial Attack, Adversarial Example, Graph Convolutional Network(GCN)
**Tutorails**
* Adversarial Attack(Part 1): Adversarial Attack과 Defense [Link](https://medium.com/@tantara/adversarial-attack-part-1-a830ec92acde)
* Adversarial Attack(Part 2): PeerNets [Link](https://medium.com/@tantara/adversarial-attack-part-2-peernets-fd5ff62818a1)
## Environement
* Docker 18.06 on Ubuntu 16.04
* PyTorch 1.0
* CUDA >= 9.1, CUDNN 7
## Experiments
- [x] LeNet5 on MNIST
- [x] ResNet32 on CIFAR10
- [x] ResNet110 on CIFAR100
- [ ] PR-LeNet5 on MNIST
- [ ] PR-ResNet32 on CIFAR10
- [ ] PR-ResNet110 on CIFAR100
### Results
* Baseline: Accuracy(%)
* rho(0.02~1.0): Accuracy(%)/Fooling Rate(%)
#### MNIST
| Method | Baseline | rho=0.2 | 0.4 | 0.6 | 0.8 | 1.0 |
| ---------------- | -------- | -------- | --------- | --------- | --------- | --------- |
| LeNet-5 | 98.6 | 92.7/7.1 | 33.9/66.0 | 14.1/85.9 | 7.9/92.2 | 8.2/91.7 |
| PR-LeNet-5 | 98.2 | 94.8/4.6 | 93.3/6.0 | 87.7/11.7 | 53.2/46.4 | 50.1/50.1 |
| LeNet-5(ours) | 98.5 | 94.2/4.0 | 75.8/32.0 | 64.5/42.0 | 57.1/50.0 | 42.0/72.0 |
| PR-LeNet-5(ours) | - | - | - | - | - | - |
#### CIFAR10
| Method | Baseline | rho=0.04 | 0.08 | 0.10 |
| --------------- | --------- | --------- | --------- | --------- |
| ResNet-32 | 92.7 | 55.3/44.4 | 26.8/73.1 | 22.7/77.3 |
| PR-ResNet-32 | 89.3 | 87.3/7.1 | 83.3/13.0 | 70.0/28.3 |
| ResNet-32(ours) | 93.3 | 84.7/20.0 | 75.2/32.0 | 69.5/46.0 |
#### CIFAR100
| Method | Baseline | rho=0.02 | 0.04 | 0.06 |
| ---------------- | -------- | --------- | --------- | --------- |
| ResNet-110 | 71.6 | 45.5/49.8 | 45.5/49.8 | 45.5/49.8 |
| PR-ResNet-110 | 66.4 | 61.5/23.7 | 52.6/38.6 | 44.6/49.5 |
| ResNet-110(ours) | 68.8 | - | - | - |
## How to run
```bash
nvidia-docker run -it tantara/peernets-pytorch /bin/bash # and CMD
nvidia-docker run -it tantara/peernets-pytorch CMD
```
- `CMD` can be Evaluation, Attack, Training as follows:
### Evaluation
```bash
# bash eval.sh GPU_ID EXP
bash eval.sh 0 lenet5 # lenet5, pr-lenet5
bash eval.sh 0 resnet32 # resnet32, pr-resnet32
bash eval.sh 0 resnet110 # resnet110, pr-resnet110
```
### Adversarial Attack
```bash
# bash attack.sh GPU_ID EXP NUM_SAMPLES RHO
bash attack.sh 0 lenet5 50 0.2 # lenet5, pr-lenet5
bash attack.sh 0 resnet32 50 0.04 # resnet32, pr-resnet32
bash attack.sh 0 resnet110 50 0.02 # resnet110, pr-resnet110
```
### Training
```bash
# bash train.sh GPU_ID EXP
bash train.sh 0 lenet5 # lenet5, pr-lenet5
bash train.sh 0 resnet32 # resnet32, pr-resnet32
bash train.sh 0 resnet110 # resnet110, pr-resnet110
```
## References
1. **PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks**
Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas. arXiv: 1806.00088.
[[link]](https://arxiv.org/abs/1806.00088). arXiv: 1806.00088, 2018.
2. [aaron-xichen/pytorch-playground](https://github.com/aaron-xichen/pytorch-playground)
3. [akamaster/pytorch_resnet_cifar10](https://github.com/akamaster/pytorch_resnet_cifar10)
4. [rusty1s/pytorch_geometric](https://github.com/rusty1s/pytorch_geometric)
## Authors
- [Taekmin Kim, @tantara](https://www.linkedin.com/in/taekminkim/)
## License
© [Taekmin Kim](https://www.linkedin.com/in/taekminkim/), 2018. Licensed under the [MIT](LICENSE) License.