A library for benchmarking vulnerability to adversarial examples

CleverHans (v1.0.0)

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Build Status

This repository contains the source code for CleverHans, a Python library to benchmark machine learning systems’ vulnerability to adversarial examples. You can learn more about such vulnerabilities on the accompanying blog.

The CleverHans library is under continual development, always welcoming contributions of the latest attacks and defenses. In particular, we always welcome help towards resolving the issues currently open.

Setting up CleverHans


This library uses TensorFlow or Theano to accelerate graph computations performed by many machine learning models. Installing these libraries is therefore a pre-requisite. You can find instructions here for Tensorflow and here for Theano. For better performance, it is also recommended to install the backend library of your choice (TensorFlow or Theano) with GPU support.

Some models used in the tutorials are also defined using Keras. Note that you should configure Keras to use the backend that matches the one used by the tutorial. Indeed, some tutorials use Tensorflow as their backend while others use Theano. You can find instructions for setting the Keras backend on this page.

Installing TensorFlow or Theano will take care of all other dependencies like numpy and scipy.


Once dependencies have been taken care of, you can install CleverHans using pip or by cloning the Github repository.

pip installation

If you are installing CleverHans using pip, run the following command:

pip install -e git+

Manual installation

If you are installing CleverHans manually, you simply need to clone this repository into a folder of your choice.

git clone

On UNIX machines, it is recommended to add your clone of this repository to the PYTHONPATH variable so as to be able to import cleverhans from any folder.

export PYTHONPATH="/path/to/cleverhans":$PYTHONPATH

You may want to make that change permanent through your shell’s profile.


To help you get started with the functionalities provided by this library, the `tutorials/’ folder comes with the following tutorials: * MNIST with FGSM using the TensorFlow backend (code, tutorial): this first tutorial covers how to train a MNIST model using TensorFlow, craft adversarial examples using the fast gradient sign method, and make the model more robust to adversarial examples using adversarial training. * MNIST with JSMA using the Tensorflow backend (code, tutorial): this second tutorial covers how to train a MNIST model using TensorFlow and craft adversarial examples using the Jacobian-based saliency map approach. * MNIST with FGSM using the Theano backend (code): this tutorial covers how to train a MNIST model using Theano, craft adversarial examples using the fast gradient sign method and make the model more robust to adversarial examples using adversarial training. Note: this script does not have a tutorial markdown yet, but the corresponding tutorial in TensorFlow will prove useful in the meanwhile. * MNIST using a black-box attack (code): this tutorial implements the black-box attack described in this paper. The adversary train a substitute model: a copy that imitates the black-box model by observing the labels that the black-box model assigns to inputs chosen carefully by the adversary. The adversary then uses the substitute model’s gradients to find adversarial examples that are misclassified by the black-box model as well.


The examples/ folder contains additional scripts to showcase different uses of the CleverHans library.

Reporting benchmarks

When reporting benchmarks, please: * Use a versioned release of CleverHans. You can find a list of released versions here. * Either use the latest version, or, if comparing to an earlier publication, use the same version as the earlier publication. * Report which attack method was used. * Report any configuration variables used to determine the behavior of the attack.

For example, you might report “We benchmarked the robustness of our method to adversarial attack using v1.0.0 of CleverHans. On a test set modified by the fgsm with eps of 0.3, we obtained a test set accuracy of 71.3%.”


Contributions are welcomed! We ask that new efforts and features be coordinated on the mailing list for CleverHans development: [email protected]. When making contributions to CleverHans, we ask that you follow the PEP8 coding style in your pull requests.

Bug fixes can be initiated through Github pull requests.

Citing this work

If you use CleverHans for academic research, you are highly encouraged (though not required) to cite the following paper:

  title={cleverhans v1.0.0: an adversarial machine learning library},
  author={Papernot, Nicolas and Goodfellow, Ian and Sheatsley, Ryan and Feinman, Reuben and McDaniel, Patrick},
  journal={arXiv preprint arXiv:1610.00768},

A new version of the technical report will be uploaded for each major revision. GitHub contributors will be added to the author list.

About the name

The name CleverHans is a reference to a presentation by Bob Sturm titled “Clever Hans, Clever Algorithms: Are Your Machine Learnings Learning What You Think?” and the corresponding publication, “A Simple Method to Determine if a Music Information Retrieval System is a ‘Horse’.” Clever Hans was a horse that appeared to have learned to answer arithmetic questions, but had in fact only learned to read social cues that enabled him to give the correct answer. In controlled settings where he could not see people’s faces or receive other feedback, he was unable to answer the same questions. The story of Clever Hans is a metaphor for machine learning systems that may achieve very high accuracy on a test set drawn from the same distribution as the training data, but that do not actually understand the underlying task and perform poorly on other inputs.


This library is managed and maintained by Ian Goodfellow (Google Brain), Nicolas Papernot (Pennsylvania State University), and Ryan Sheatsley (Pennsylvania State University).

The following authors contributed (ordered according to the GitHub contributors page): * Nicolas Papernot (Pennsylvania State University) * Ian Goodfellow (OpenAI) * Ryan Sheatsley (Pennsylvania State University) * Reuben Feinman (Symantec)


Copyright 2017 - Google Inc., OpenAI and Pennsylvania State University.

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