Advertorch

<a href="https://github.com/borealisai/advertorch" target="_blank"><img src="https://raw.githubusercontent.com/borealisai/advertorch/master/assets/logo.png?raw=true" alt="advertorch logo" width="200"></a>

<a href="https://github.com/borealisai/advertorch" target="_blank"><img src="https://raw.githubusercontent.com/borealisai/advertorch/master/assets/advertorch.png?raw=true" alt="advertorch text" width="100"></a> is a Python toolbox for adversarial robustness research. The primary functionalities are implemented in PyTorch. Specifically, AdverTorch contains modules for generating adversarial perturbations and defending against adversarial examples, also scripts for adversarial training.

Latest version (v0.2)

Installation

Installing AdverTorch itself

We developed AdverTorch under Python 3.6 and PyTorch 1.0.0 & 0.4.1. To install AdverTorch, simply run

pip install advertorch

or clone the repo and run

python setup.py install

To install the package in "editable" mode:

pip install -e .

Setting up the testing environments

Some attacks are tested against implementations in Foolbox or CleverHans to ensure correctness. Currently, they are tested under the following versions of related libraries.

conda install -c anaconda tensorflow-gpu==1.11.0
pip install git+https://github.com/tensorflow/cleverhans.git@336b9f4ed95dccc7f0d12d338c2038c53786ab70
pip install Keras==2.2.2
pip install foolbox==1.3.2

Examples

# prepare your pytorch model as "model"
# prepare a batch of data and label as "cln_data" and "true_label"
# ...

from advertorch.attacks import LinfPGDAttack

adversary = LinfPGDAttack(
    model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.3,
    nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0,
    targeted=False)

adv_untargeted = adversary.perturb(cln_data, true_label)

target = torch.ones_like(true_label) * 3
adversary.targeted = True
adv_targeted = adversary.perturb(cln_data, target)

For runnable examples see advertorch_examples/tutorial_attack_defense_bpda_mnist.ipynb for how to attack and defend; see advertorch_examples/tutorial_train_mnist.py for how to adversarially train a robust model on MNIST.

Documentation

The documentation webpage is on readthedocs https://advertorch.readthedocs.io.

Coming Soon

AdverTorch is still under active development. We will add the following features/items down the road:

• more examples
• support for other machine learning frameworks, e.g. TensorFlow
• more attacks, defenses and other related functionalities
• support for other Python versions and future PyTorch versions
• contributing guidelines
• ...

Known issues

FastFeatureAttack and JacobianSaliencyMapAttack do not pass the tests against the version of CleverHans used. (They use to pass tests on a previous version of CleverHans.) This issue is being investigated. In the file test_attacks_on_cleverhans.py, they are marked as "skipped" in pytest tests.

License

This project is licensed under the LGPL. The terms and conditions can be found in the LICENSE and LICENSE.GPL files.

Citation

If you use AdverTorch in your research, we kindly ask that you cite the following technical report:

@article{ding2019advertorch,
  title={{AdverTorch} v0.1: An Adversarial Robustness Toolbox based on PyTorch},
  author={Ding, Gavin Weiguang and Wang, Luyu and Jin, Xiaomeng},
  journal={arXiv preprint arXiv:1902.07623},
  year={2019}
}

Contributors

• Gavin Weiguang Ding
• Luyu Wang
• Xiaomeng Jin
• Laurent Meunier
• Alexandre Araujo
• Jérôme Rony
• Ben Feinstein
• Francesco Croce
• Taro Kiritani