Skip to main content
Donate to the Python Software Foundation or Purchase a PyCharm License to Benefit the PSF! Donate Now

Python toolbox to create adversarial examples that fool neural networks

Project description

You might want to have a look at our recently announced Robust Vision Benchmark.


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 as np
from keras.applications.resnet50 import ResNet50

# instantiate model
kmodel = ResNet50(weights='imagenet')
preprocessing = (np.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
# ::-1 reverses the color channels, because Keras ResNet50 expects BGR instead of RGB
attack = foolbox.attacks.FGSM(fmodel)
adversarial = attack(image[:, :, ::-1], label)

The result can be plotted like this:

# if you use Jupyter notebooks
%matplotlib inline

import matplotlib.pyplot as plt


plt.subplot(1, 3, 1)
plt.imshow(image / 255)  # division by 255 to convert [0, 255] to [0, 1]

plt.subplot(1, 3, 2)
plt.imshow(adversarial[:, :, ::-1] / 255)  # ::-1 to convert BGR to RGB

plt.subplot(1, 3, 3)
difference = adversarial[:, :, ::-1] - image
plt.imshow(difference / abs(difference).max() * 0.2 + 0.5)

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 arXiv:1707.04131},
  archivePrefix = "arXiv",

You can find the paper on arXiv:

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
foolbox-0.11.1.tar.gz (215.7 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page