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Python toolbox to create adversarial examples that fool neural networks

Project description


Foolbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python, NumPy and SciPy.


pip install foolbox

We test using Python 2.7, 3.5 and 3.6. Other Python versions might work as well. We recommend using Python 3!


Documentation is available on readthedocs:

Our paper describing Foolbox is on arXiv:


import foolbox
import keras
import numpy
from keras.applications.resnet50 import ResNet50

# instantiate model
kmodel = ResNet50(weights='imagenet')
preprocessing = (numpy.array([104, 116, 123]), 1)
fmodel = foolbox.models.KerasModel(kmodel, bounds=(0, 255), preprocessing=preprocessing)

# get source image and label
image, label = foolbox.utils.imagenet_example()

# apply attack on source image
attack  = foolbox.attacks.FGSM(fmodel)
adversarial = attack(image[:,:,::-1], label)

Interfaces for a range of other deeplearning packages such as TensorFlow, PyTorch, Theano, Lasagne and MXNet are available, e.g.

model = foolbox.models.TensorFlowModel(images, logits, bounds=(0, 255))
model = foolbox.models.PyTorchModel(torchmodel, bounds=(0, 255), num_classes=1000)
# etc.

Different adversarial criteria such as Top-k, specific target classes or target probability values for the original class or the target class can be passed to the attack, e.g.

criterion = foolbox.criteria.TargetClass(22)
attack    = foolbox.attacks.LBFGSAttack(fmodel, criterion)

Feature requests and bug reports

We welcome feature requests and bug reports. Just create a new issue on GitHub.


Depending on the nature of your question feel free to post it as an issue on GitHub, or post it as a question on Stack Overflow using the foolbox tag. We will try to monitor that tag but if you don’t get an answer don’t hesitate to contact us.

Contributions welcome

Foolbox is a work in progress and any input is welcome.

In particular, we encourage users of deep learning frameworks for which we do not yet have builtin support, e.g. Caffe, Caffe2 or CNTK, to contribute the necessary wrappers. Don’t hestiate to contact us if we can be of any help.

Moreoever, attack developers are encouraged to share their reference implementation using Foolbox so that it will be available to everyone.


If you find Foolbox useful for your scientific work, please consider citing it in resulting publications:

  title={Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models},
  author={Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
  journal={arXiv preprint},

You can find the paper on arXiv:

Project details

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