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https://github.com/DSE-MSU/DeepRobust
A pytorch adversarial library for attack and defense methods on images and graphs
https://github.com/DSE-MSU/DeepRobust
adversarial-attacks adversarial-examples deep-learning deep-neural-networks defense graph-convolutional-networks graph-mining graph-neural-networks machine-learning
Last synced: 24 days ago
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A pytorch adversarial library for attack and defense methods on images and graphs
- Host: GitHub
- URL: https://github.com/DSE-MSU/DeepRobust
- Owner: DSE-MSU
- License: mit
- Created: 2019-09-21T16:09:07.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-23T13:21:18.000Z (5 months ago)
- Last Synced: 2024-10-17T06:24:28.072Z (about 2 months ago)
- Topics: adversarial-attacks, adversarial-examples, deep-learning, deep-neural-networks, defense, graph-convolutional-networks, graph-mining, graph-neural-networks, machine-learning
- Language: Python
- Homepage:
- Size: 11.9 MB
- Stars: 977
- Watchers: 16
- Forks: 189
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-graph-attack-papers - https://github.com/DSE-MSU/DeepRobust
- StarryDivineSky - DSE-MSU/DeepRobust
README
[contributing-image]: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
[contributing-url]: https://github.com/rusty1s/pytorch_geometric/blob/master/CONTRIBUTING.md
---------------------
[![Contributing][contributing-image]][contributing-url]
[](https://twitter.com/intent/tweet?text=Build%20your%20robust%20machine%20learning%20models%20with%20DeepRobust%20in%2060%20seconds&url=https://github.com/DSE-MSU/DeepRobust&via=dse_msu&hashtags=MachineLearning,DeepLearning,secruity,data,developers)
**[Documentation](https://deeprobust.readthedocs.io/en/latest/)** | **[Paper](https://arxiv.org/abs/2005.06149)** | **[Samples](https://github.com/DSE-MSU/DeepRobust/tree/master/examples)**
[AAAI 2021] DeepRobust is a PyTorch adversarial library for attack and defense methods on images and graphs.
* If you are new to DeepRobust, we highly suggest you read the [documentation page](https://deeprobust.readthedocs.io/en/latest/) or the following content in this README to learn how to use it.
* If you have any questions or suggestions regarding this library, feel free to create an issue [here](https://github.com/DSE-MSU/DeepRobust/issues). We will reply as soon as possible :)
**List of including algorithms can be found in [[Image Package]](https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/image) and [[Graph Package]](https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph).**
[Environment & Installation](#environment)
Usage
* [Image Attack and Defense](#image-attack-and-defense)
* [Graph Attack and Defense](#graph-attack-and-defense)
[Acknowledgement](#acknowledgement)
For more details about attacks and defenses, you can read the following papers.
* [Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies](https://arxiv.org/abs/2003.00653)
* [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://arxiv.org/pdf/1909.08072.pdf)
If our work could help your research, please cite:
[DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses](https://arxiv.org/abs/2005.06149)
```
@article{li2020deeprobust,
title={Deeprobust: A pytorch library for adversarial attacks and defenses},
author={Li, Yaxin and Jin, Wei and Xu, Han and Tang, Jiliang},
journal={arXiv preprint arXiv:2005.06149},
year={2020}
}
```
# Changelog
* [11/2023] Try `git clone https://github.com/DSE-MSU/DeepRobust.git; cd DeepRobust; python setup_empty.py install` to directly install DeepRobust without installing dependency packages.
* [11/2023] DeepRobust 0.2.9 Released. Please try `pip install deeprobust==0.2.9`. We have fixed the OOM issue of metattack on new pytorch versions.
* [06/2023] We have added a backdoor attack [UGBA, WWW'23](https://arxiv.org/abs/2303.01263) to graph package. We can now use UGBA to conduct unnoticeable backdoor attack on large-scale graphs such as ogb-arxiv (see example in [test_ugba.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_ugba.py))!
* [02/2023] DeepRobust 0.2.8 Released. Please try `pip install deeprobust==0.2.8`! We have added a scalable attack [PRBCD, NeurIPS'21](https://arxiv.org/abs/2110.14038) to graph package. We can now use PRBCD to attack large-scale graphs such as ogb-arxiv (see example in [test_prbcd.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_prbcd.py))!
* [02/2023] Add a robust model [AirGNN, NeurIPS'21](https://proceedings.neurips.cc/paper/2021/file/50abc3e730e36b387ca8e02c26dc0a22-Paper.pdf) to graph package. Try `python examples/graph/test_airgnn.py`! See details in [test_airgnn.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_airgnn.py)
* [11/2022] DeepRobust 0.2.6 Released. Please try `pip install deeprobust==0.2.6`! We have more updates coming. Please stay tuned!
* [11/2021] A subpackage that includes popular black box attacks in image domain is released. Find it here. [Link](https://github.com/I-am-Bot/Black-Box-Attacks)
* [11/2021] DeepRobust 0.2.4 Released. Please try `pip install deeprobust==0.2.4`!
* [10/2021] add scalable attack and MedianGCN. Thank [Jintang](https://github.com/EdisonLeeeee) for his contribution!
* [06/2021] [Image Package] Add preprocessing method: APE-GAN.
* [05/2021] DeepRobust is published at AAAI 2021. Check [here](https://ojs.aaai.org/index.php/AAAI/article/view/18017)!
* [05/2021] DeepRobust 0.2.2 Released. Please try `pip install deeprobust==0.2.2`!
* [04/2021] [Image Package] Add support for ImageNet. See details in [test_ImageNet.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/image/test_ImageNet.py)
* [04/2021] [Graph Package] Add support for OGB datasets. See more details in the [tutorial page](https://deeprobust.readthedocs.io/en/latest/graph/pyg.html).
* [03/2021] [Graph Package] Added node embedding attack and victim models! See this [tutorial page](https://deeprobust.readthedocs.io/en/latest/graph/node_embedding.html).
* [02/2021] **[Graph Package] DeepRobust now provides tools for converting the datasets between [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) and DeepRobust. See more details in the [tutorial page](https://deeprobust.readthedocs.io/en/latest/graph/pyg.html)!** DeepRobust now also support GAT, Chebnet and SGC based on pyg; see details in [test_gat.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_gat.py), [test_chebnet.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_chebnet.py) and [test_sgc.py](https://github.com/DSE-MSU/DeepRobust/blob/master/examples/graph/test_sgc.py)
* [12/2020] DeepRobust now can be installed via pip! Try `pip install deeprobust`!
* [12/2020] [Graph Package] Add four more [datasets](https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph/#supported-datasets) and one defense algorithm. More details can be found [here](https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph/#defense-methods). More datasets and algorithms will be added later. Stay tuned :)
* [07/2020] Add [documentation](https://deeprobust.readthedocs.io/en/latest/) page!
* [06/2020] Add docstring to both image and graph package
# Basic Environment
* `python >= 3.6` (python 3.5 should also work)
* `pytorch >= 1.2.0`
see `setup.py` or `requirements.txt` for more information.
# Installation
## Install from pip
```
pip install deeprobust
```
## Install from source
```
git clone https://github.com/DSE-MSU/DeepRobust.git
cd DeepRobust
python setup.py install
```
If you find the dependencies are hard to install, please try the following:
```python setup_empty.py install``` (only install deeprobust without installing other packages)
# Test Examples
```
python examples/image/test_PGD.py
python examples/image/test_pgdtraining.py
python examples/graph/test_gcn_jaccard.py --dataset cora
python examples/graph/test_mettack.py --dataset cora --ptb_rate 0.05
```
# Usage
## Image Attack and Defense
1. Train model
Example: Train a simple CNN model on MNIST dataset for 20 epoch on gpu.
```python
import deeprobust.image.netmodels.train_model as trainmodel
trainmodel.train('CNN', 'MNIST', 'cuda', 20)
```
Model would be saved in deeprobust/trained_models/.
2. Instantiated attack methods and defense methods.
Example: Generate adversary example with PGD attack.
```python
from deeprobust.image.attack.pgd import PGD
from deeprobust.image.config import attack_params
from deeprobust.image.utils import download_model
import torch
import deeprobust.image.netmodels.resnet as resnet
from torchvision import transforms,datasets
URL = "https://github.com/I-am-Bot/deeprobust_model/raw/master/CIFAR10_ResNet18_epoch_20.pt"
download_model(URL, "$MODEL_PATH$")
model = resnet.ResNet18().to('cuda')
model.load_state_dict(torch.load("$MODEL_PATH$"))
model.eval()
transform_val = transforms.Compose([transforms.ToTensor()])
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('deeprobust/image/data', train = False, download=True,
transform = transform_val),
batch_size = 10, shuffle=True)
x, y = next(iter(test_loader))
x = x.to('cuda').float()
adversary = PGD(model, 'cuda')
Adv_img = adversary.generate(x, y, **attack_params['PGD_CIFAR10'])
```
Example: Train defense model.
```python
from deeprobust.image.defense.pgdtraining import PGDtraining
from deeprobust.image.config import defense_params
from deeprobust.image.netmodels.CNN import Net
import torch
from torchvision import datasets, transforms
model = Net()
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('deeprobust/image/defense/data', train=True, download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=100,shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('deeprobust/image/defense/data', train=False,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=1000,shuffle=True)
defense = PGDtraining(model, 'cuda')
defense.generate(train_loader, test_loader, **defense_params["PGDtraining_MNIST"])
```
More example code can be found in deeprobust/examples.
3. Use our evulation program to test attack algorithm against defense.
Example:
```
cd DeepRobust
python examples/image/test_train.py
python deeprobust/image/evaluation_attack.py
```
## Graph Attack and Defense
### Attacking Graph Neural Networks
1. Load dataset
```python
import torch
import numpy as np
from deeprobust.graph.data import Dataset
from deeprobust.graph.defense import GCN
from deeprobust.graph.global_attack import Metattack
data = Dataset(root='/tmp/', name='cora', setting='nettack')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
idx_unlabeled = np.union1d(idx_val, idx_test)
```
2. Set up surrogate model
```python
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
surrogate = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1, nhid=16,
with_relu=False, device=device)
surrogate = surrogate.to(device)
surrogate.fit(features, adj, labels, idx_train)
```
3. Set up attack model and generate perturbations
```python
model = Metattack(model=surrogate, nnodes=adj.shape[0], feature_shape=features.shape, device=device)
model = model.to(device)
perturbations = int(0.05 * (adj.sum() // 2))
model.attack(features, adj, labels, idx_train, idx_unlabeled, perturbations, ll_constraint=False)
modified_adj = model.modified_adj
```
For more details please refer to [mettack.py](https://github.com/I-am-Bot/DeepRobust/blob/master/examples/graph/test_mettack.py) or run
```
python examples/graph/test_mettack.py --dataset cora --ptb_rate 0.05
```
### Defending Against Graph Attacks
1. Load dataset
```python
import torch
from deeprobust.graph.data import Dataset, PtbDataset
from deeprobust.graph.defense import GCN, GCNJaccard
import numpy as np
np.random.seed(15)
# load clean graph
data = Dataset(root='/tmp/', name='cora', setting='nettack')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
# load pre-attacked graph by mettack
perturbed_data = PtbDataset(root='/tmp/', name='cora')
perturbed_adj = perturbed_data.adj
```
2. Test
```python
# Set up defense model and test performance
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = GCNJaccard(nfeat=features.shape[1], nclass=labels.max()+1, nhid=16, device=device)
model = model.to(device)
model.fit(features, perturbed_adj, labels, idx_train)
model.eval()
output = model.test(idx_test)
# Test on GCN
model = GCN(nfeat=features.shape[1], nclass=labels.max()+1, nhid=16, device=device)
model = model.to(device)
model.fit(features, perturbed_adj, labels, idx_train)
model.eval()
output = model.test(idx_test)
```
For more details please refer to [test_gcn_jaccard.py](https://github.com/I-am-Bot/DeepRobust/blob/master/examples/graph/test_gcn_jaccard.py) or run
```
python examples/graph/test_gcn_jaccard.py --dataset cora
```
## Sample Results
adversary examples generated by fgsm:
Left:original, classified as 6; Right:adversary, classified as 4.
Serveral trained models can be found here: https://drive.google.com/open?id=1uGLiuCyd8zCAQ8tPz9DDUQH6zm-C4tEL
## Acknowledgement
Some of the algorithms are referred to paper authors' implementations. References can be found at the top of each file.
Implementation of network structure are referred to weiaicunzai's github. Original code can be found here:
[pytorch-cifar100](https://github.com/weiaicunzai/pytorch-cifar100)
Thanks to their outstanding works!