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.
Project description
Foolbox Native
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. 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
pip install --upgrade foolbox-native
PyTorch Example
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
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))
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
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