def test_attack_eg(request, bn_model, bn_criterion, bn_images, bn_labels):
GradientEstimator = request.param
gradient_estimator = GradientEstimator(epsilon=0.01)
model = ModelWithEstimatedGradients(bn_model, gradient_estimator)
attack = Attack(model, bn_criterion)
advs = attack(bn_images, bn_labels, unpack=False)
for adv in advs:
assert adv.perturbed is not None
assert adv.distance.value < np.inf
def eg_bn_model():
cm_model = contextmanager(bn_model)
with cm_model() as model:
gradient_estimator = GradientEstimator(epsilon=0.01)
model = ModelWithEstimatedGradients(model, gradient_estimator)
yield model