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PeerNets on PyTorch 1.0
A pytorch implementation of PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Keywords
Adversarial Attack, Adversarial Example, Graph Convolutional Network(GCN)
Tutorails
- Adversarial Attack(Part 1): Adversarial Attack과 Defense Link
- Adversarial Attack(Part 2): PeerNets Link
Environement
- Docker 18.06 on Ubuntu 16.04
- PyTorch 1.0
- CUDA >= 9.1, CUDNN 7
Experiments
- LeNet5 on MNIST
- ResNet32 on CIFAR10
- 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
nvidia-docker run -it tantara/peernets-pytorch /bin/bash # and CMD
nvidia-docker run -it tantara/peernets-pytorch CMD
CMDcan be Evaluation, Attack, Training as follows:
Evaluation
# 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 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 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
-
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks<br>
Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas. arXiv: 1806.00088.<br>
[link]. arXiv: 1806.00088, 2018.
Authors
License
© Taekmin Kim, 2018. Licensed under the MIT License.