Python toolbox to create adversarial examples that fool neural networks
Project description
Foolbox
Foolbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python 3, NumPy and SciPy.
Installation
pip install foolbox
Documentation
Documentation is available on readthedocs: http://foolbox.readthedocs.io/
Example
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.
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.
criterion = foolbox.criteria.TargetClass(22)
attack = foolbox.attacks.FGSM(fmodel, criterion)
Development
Foolbox is a work in progress and any input is welcome.
Project details
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foolbox-0.3.4.tar.gz
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