Advanced

The Adversarial class provides an advanced way to specify the adversarial example that should be found by an attack and provides detailed information about the created adversarial. In addition, it provides a way to improve a previously found adversarial example by re-running an attack.

Implicit

model = TensorFlowModel(images, logits, bounds=(0, 255))
criterion = TargetClassProbability('ostrich', p=0.99)
attack = LBFGSBAttack(model, criterion)

Running the attack by passing image and label will implicitly create an Adversarial instance. By passing unpack=False we tell the attack to return the Adversarial instance rather than the actual image.

adversarial = attack(image=image, label=label, unpack=False)

We can then get the actual image using the image attribute:

adversarial_image = adversarial.image

Explicit

model = TensorFlowModel(images, logits, bounds=(0, 255))
criterion = TargetClassProbability('ostrich', p=0.99)
attack = LBFGSBAttack()

We can also create the Adversarial instance ourselves and then pass it to the attack.

adversarial = Adversarial(model, criterion, image, label)
attack(adversarial)

Again, we can get the image using the image attribute:

adversarial_image = adversarial.image

This approach gives us more flexibility and allows us to specify a different distance measure:

distance = MeanAbsoluteDistance
adversarial = Adversarial(model, criterion, image, label, distance=distance)