Metadata-Version: 1.1
Name: foolbox
Version: 0.3.3
Summary: Python toolbox to create adversarial examples that fool neural networks
Home-page: https://github.com/bethgelab/foolbox
Author: Jonas Rauber & Wieland Brendel
Author-email: opensource@bethgelab.org
License: MIT
Description: .. image:: https://readthedocs.org/projects/foolbox/badge/?version=latest
            :target: https://foolbox.readthedocs.io/en/latest/
        
        .. image:: https://travis-ci.org/bethgelab/foolbox.svg?branch=master
            :target: https://travis-ci.org/bethgelab/foolbox
        
        .. image:: https://coveralls.io/repos/github/bethgelab/foolbox/badge.svg
            :target: https://coveralls.io/github/bethgelab/foolbox
        
        
        
        =======
        Foolbox
        =======
        
        Foolbox is a Python toolbox to create adversarial examples that fool neural networks. It requires `Python 3`, `NumPy` and `SciPy`.
        
        Installation
        ------------
        
        .. code-block:: bash
        
           pip install foolbox
        
        Documentation
        -------------
        
        Documentation is available on readthedocs: http://foolbox.readthedocs.io/
        
        Example
        -------
        
        .. code-block:: python
        
           import foolbox
           import keras
           from keras.applications.resnet50 import ResNet50, preprocess_input
        
           # instantiate model
           keras.backend.set_learning_phase(0)
           kmodel = ResNet50(weights='imagenet')
           fmodel = foolbox.models.KerasModel(kmodel, bounds=(0, 255), preprocess_fn=preprocess_input)
        
           # get source image and label
           image, label = foolbox.utils.imagenet_example()
        
           # apply attack on source image
           attack  = foolbox.attacks.FGSM(fmodel)
           adv_img = attack(image=image, label=label)
        
        Interfaces for a range of other deeplearning packages such as TensorFlow, 
        PyTorch and Lasagne are available, e.g.
        
        .. code-block:: python
        
           model = foolbox.models.PyTorchModel(torchmodel)
        
        Different adversarial criteria such as Top-k, specific target classes or target probability 
        levels can be passed to the attack, e.g.
        
        .. code-block:: python
        
           criterion = foolbox.criteria.TargetClass(22)
           attack    = foolbox.attacks.FGSM(fmodel, criterion)
        
        Development
        -----------
        
        Foolbox is a work in progress and any input is welcome.
        
        Authors
        -------
        
        * Jonas Rauber
        * Wieland Brendel
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
