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 Criterion class.

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.subplot(1, 3, 2)

plt.subplot(1, 3, 3)
plt.imshow(adversarial - image)

External Resources

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