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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

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A pytorch adversarial library for attack and defense methods on images and graphs

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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





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GitHub last commit  GitHub issues GitHub

[![Contributing][contributing-image]][contributing-url]

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**[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!