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توضیحات

A PyTorch library for adversarial robustness learning for image and graph data.

ویژگی	مقدار
سیستم عامل	-
نام فایل	deeprobust-0.2.8
نام	deeprobust
نسخه کتابخانه	0.2.8
نگهدارنده	['MSU-DSE']
ایمیل نگهدارنده	[]
نویسنده	MSU-DSE
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/DSE-MSU/DeepRobust
آدرس اینترنتی	https://pypi.org/project/deeprobust/
مجوز	MIT

[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 <p align="center"> <img center src="https://github.com/DSE-MSU/DeepRobust/blob/master/adversary_examples/Deeprobust.png" width = "450" alt="logo"> </p> ---------------------  <img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/DSE-MSU/DeepRobust"> <a href="https://github.com/DSE-MSU/DeepRobust/issues"> <img alt="GitHub issues" src="https://img.shields.io/github/issues/DSE-MSU/DeepRobust"></a> <img alt="GitHub" src="https://img.shields.io/github/license/DSE-MSU/DeepRobust"> [![Contributing][contributing-image]][contributing-url] [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](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 :) <p float="left"> <img src="https://github.com/DSE-MSU/DeepRobust/blob/master/adversary_examples/adversarial.png" width="430" /> <img src="https://github.com/DSE-MSU/DeepRobust/blob/master/adversary_examples/graph_attack_example.png" width="380" /> </p> **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 * [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 relased. 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 ``` # 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: <div align="center"> <img height=140 src="https://github.com/DSE-MSU/DeepRobust/blob/master/adversary_examples/mnist_advexample_fgsm_ori.png"/><img height=140 src="https://github.com/DSE-MSU/DeepRobust/blob/master/adversary_examples/mnist_advexample_fgsm_adv.png"/> </div> 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!

نیازمندی

مقدار	نام
>=3.1.1	matplotlib
>=1.17.1	numpy
>=1.2.0	torch
>=1.3.1	scipy
>=0.4.0	torchvision
>=1.6.2	texttable
>=2.4	networkx
>=0.48.0	numba
>=7.0.0	Pillow
>=0.22.1	scikit-learn
>=0.0	scikit-image
>=2.0	tensorboardX
>=3.0	tqdm
<4.0,>=3.8	gensim

نحوه نصب

نصب پکیج whl deeprobust-0.2.8:

pip install deeprobust-0.2.8.whl

نصب پکیج tar.gz deeprobust-0.2.8:

pip install deeprobust-0.2.8.tar.gz