def test_classifier_match(self):
attack = FastGradientMethod(self.classifier)
adv_trainer = AdversarialTrainer(self.classifier, attack)
self.assertEqual(len(adv_trainer.attacks), 1)
self.assertEqual(adv_trainer.attacks[0].estimator,
adv_trainer.get_classifier())
class AdversarialTrainerMadryPGD(Trainer):
"""
Class performing adversarial training following Madry's Protocol.
Paper link: https://arxiv.org/abs/1706.06083
Please keep in mind the limitations of defences. While adversarial training is
widely regarded as a promising, principled approach to making classifiers more
robust (see https://arxiv.org/abs/1802.00420), very careful evaluations are
required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
"""
def __init__(self,
classifier,
eps=0.03,
eps_step=0.008,
max_iter=7,
ratio=1.0):
self.attack = ProjectedGradientDescent(
classifier,
eps=eps,
eps_step=eps_step,
max_iter=max_iter,
)
self.trainer = AdversarialTrainer(classifier, self.attack, ratio=ratio)
def fit(self, x, y, **kwargs):
self.trainer.fit(x, y, **kwargs)
def fit_generator(self, generator, nb_epochs, **kwargs):
self.trainer.fit_generator(generator, nb_epochs=nb_epochs, **kwargs)
def get_classifier(self):
return self.trainer.get_classifier()
class AdversarialTrainerMadryPGD(Trainer):
"""
Class performing adversarial training following Madry's Protocol.
| Paper link: https://arxiv.org/abs/1706.06083
| Please keep in mind the limitations of defences. While adversarial training is widely regarded as a promising,
principled approach to making classifiers more robust (see https://arxiv.org/abs/1802.00420), very careful
evaluations are required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
"""
def __init__(
self,
classifier: "ClassifierGradients",
nb_epochs: int = 391,
batch_size: int = 128,
eps: float = 8.0,
eps_step: float = 2.0,
max_iter: int = 7,
num_random_init: Union[bool, int] = True,
) -> None:
"""
Create an :class:`.AdversarialTrainerMadryPGD` instance.
Default values are for CIFAR-10 in pixel range 0-255.
:param classifier: Classifier to train adversarially.
:param nb_epochs: Number of training epochs.
:param batch_size: Size of the batch on which adversarial samples are generated.
:param eps: Maximum perturbation that the attacker can introduce.
:param eps_step: Attack step size (input variation) at each iteration.
:param max_iter: The maximum number of iterations.
:param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0
starting at the original input.
"""
super(AdversarialTrainerMadryPGD, self).__init__(classifier=classifier) # type: ignore
self.batch_size = batch_size
self.nb_epochs = nb_epochs
# Setting up adversary and perform adversarial training:
self.attack = ProjectedGradientDescent(
classifier, eps=eps, eps_step=eps_step, max_iter=max_iter, num_random_init=num_random_init,
)
self.trainer = AdversarialTrainer(classifier, self.attack, ratio=1.0) # type: ignore
def fit(self, x: np.ndarray, y: np.ndarray, validation_data: Optional[np.ndarray] = None, **kwargs) -> None:
"""
Train a model adversarially. See class documentation for more information on the exact procedure.
:param x: Training data.
:param y: Labels for the training data.
:param validation_data: Validation data.
:param kwargs: Dictionary of framework-specific arguments.
"""
self.trainer.fit(
x, y, validation_data=validation_data, nb_epochs=self.nb_epochs, batch_size=self.batch_size, **kwargs
)
def get_classifier(self) -> "Classifier":
return self.trainer.get_classifier()