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foolbox-native-0.8.0
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مشاهده بیشترتوضیحات
Foolbox Native is an extension for Foolbox that tries to bring native performance to Foolbox. This extension is a prototype with the goal of ultimately becoming part of Foolbox itself.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | foolbox-native-0.8.0 |
| نام | foolbox-native |
| نسخه کتابخانه | 0.8.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jonas Rauber |
| ایمیل نویسنده | git@jonasrauber.de |
| آدرس صفحه اصلی | https://github.com/jonasrauber/foolbox-native |
| آدرس اینترنتی | https://pypi.org/project/foolbox-native/ |
| مجوز | - |
.. image:: https://badge.fury.io/py/foolbox-native.svg
:target: https://badge.fury.io/py/foolbox-native
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/ambv/black
==============
Foolbox Native
==============
Foolbox Native is an extension for `Foolbox <https://github.com/bethgelab/foolbox>`_
that tries to bring native performance to Foolbox. This extension is a
prototype with the goal of ultimately becoming part of Foolbox itself.
Please be aware of the the differences to Foolbox listed below.
Foolbox Native currently provides full support for:
* PyTorch
* TensorFlow
* JAX
Other frameworks can be used as well by falling back to Foolbox for the
model API, see below.
Installation
------------
.. code-block:: bash
pip install --upgrade foolbox-native
PyTorch Example
---------------
.. code-block:: python
import foolbox.ext.native as fbn
import torchvision.models as models
# instantiate a model
model = models.resnet18(pretrained=True).eval()
preprocessing = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], axis=-3)
fmodel = fbn.models.PyTorchModel(model, bounds=(0, 1), preprocessing=preprocessing)
# get data and test the model
images, labels = fbn.utils.samples(fmodel, dataset='imagenet', batchsize=16)
print(fbn.utils.accuracy(fmodel, images, labels))
# -> 0.9375
# apply the attack
attack = fbn.attacks.LinfinityBasicIterativeAttack(fmodel)
adversarials = attack(images, labels, epsilon=0.03, step_size=0.005) # L-inf norm
print(fbn.utils.accuracy(fmodel, adversarials, labels))
# -> 0.0
# apply another attack
attack = fbn.attacks.L2BasicIterativeAttack(fmodel)
adversarials = attack(images, labels, epsilon=2.0, step_size=0.2) # L2 norm
print(fbn.utils.accuracy(fmodel, adversarials, labels))
# -> 0.0
TensorFlow Example
------------------
.. code-block:: python
import foolbox.ext.native as fbn
import tensorflow as tf
# instantiate a model
model = tf.keras.applications.ResNet50(weights='imagenet')
preprocessing = dict(flip_axis=-1, mean=[104., 116., 123.]) # RGB to BGR
fmodel = fbn.models.TensorFlowModel(model, bounds=(0, 255), preprocessing=preprocessing)
# get data and test the model
images, labels = fbn.utils.samples(fmodel, dataset='imagenet', batchsize=16)
print(fbn.utils.accuracy(fmodel, images, labels))
# apply the attack
attack = fbn.attacks.LinfinityBasicIterativeAttack(fmodel)
adversarials = attack(images, labels, epsilon=0.03 * 255., step_size=0.005 * 255.) # L-inf norm
print(fbn.utils.accuracy(fmodel, adversarials, labels))
# apply another attack
attack = fbn.attacks.L2BasicIterativeAttack(fmodel)
adversarials = attack(images, labels, epsilon=2.0 * 255., step_size=0.2 * 255.) # L2 norm
print(fbn.utils.accuracy(fmodel, adversarials, labels))
Robust Accuracy Evaluation
--------------------------
.. code-block:: python
import foolbox.ext.native as fbn
# get fmodel, images, labels like above
fmodel = ...
images, labels = ...
attacks = [
L2BasicIterativeAttack,
L2CarliniWagnerAttack,
L2ContrastReductionAttack,
BinarySearchContrastReductionAttack,
LinearSearchContrastReductionAttack,
]
epsilons = [0.0, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0]
_, robust_accuracy = fbn.evaluate_l2(fmodel, x, y, attacks=attacks, epsilons=epsilons)
print(robust_accuracy)
# Plot an accuracy-distortion curve
plt.plot(epsilons, robust_accuracy)
Other Frameworks
----------------
Foolbox Native supports all frameworks supported by the standard Foolbox
by simply wrapping the ``foolbox.models.*`` classes using ``fbn.model.FoolboxModel``.
This, however, comes with a performance penalty. Nevertheless, it still
allows one to profit from the manually batched attack reimplementations
that come with Foolbox Native.
Important differences to Foolbox
--------------------------------
Unlike Foolbox:
* Foolbox Native does not yet guarantee API stability (expect breaking changes)
* Foolbox Native is currently limited to very few attacks
* Foolbox Native does not make any guarantees about the output of an attack
* The user is responsible for checking if the returned samples are adversarial
* Whether the size of the perturbations is guaranteed depends on the attack
* Foolbox, on the other hand, searches for the smallest perturbation while
guaranteeing that the returned samples are adversarial
نحوه نصب
نصب پکیج whl foolbox-native-0.8.0:
pip install foolbox-native-0.8.0.whl
نصب پکیج tar.gz foolbox-native-0.8.0:
pip install foolbox-native-0.8.0.tar.gz