This tutorial will show you how an adversarial attack can be used to find adversarial examples for a model.
Creating a model¶
For the tutorial, we will target VGG19 implemented in TensorFlow, but it is straight forward to apply the same to other models or other frameworks such as Theano or PyTorch.
import tensorflow as tf images = tf.placeholder(tf.float32, (None, 224, 224, 3)) preprocessed = vgg_preprocessing(images) logits = vgg19(preprocessed)
To turn a model represented as a standard TensorFlow graph into a model that can be attacked by the Adversarial Toolbox, all we have to do is to create a new TensorFlowModel instance:
from foolbox.models import TensorFlowModel model = TensorFlowModel(images, logits, bounds=(0, 255))
Specifying the criterion¶
To run an adversarial attack, we need to specify the type of adversarial we are looking for. This can be done using the
from foolbox.criteria import TargetClassProbability target_class = 22 criterion = TargetClassProbability(target_class, p=0.99)
Running the attack¶
Finally, we can create and apply the attack:
from foolbox.attacks import LBFGSAttack attack = LBFGSAttack(model, criterion) images, labels = foolbox.utils.samples(dataset='imagenet', batchsize=16, data_format='channels_last', bounds=(0, 255)) adversarial = attack(image, label=label)
Visualizing the adversarial examples¶
To plot the adversarial example we can use matplotlib:
import matplotlib.pyplot as plt plt.subplot(1, 3, 1) plt.imshow(image) plt.subplot(1, 3, 2) plt.imshow(adversarial) plt.subplot(1, 3, 3) plt.imshow(adversarial - image)