def test_misclassfication():
c = criteria.Misclassification()
predictions = np.array([0.1, 0.5, 0.7, 0.4])
assert c.is_adversarial(predictions, 0)
assert c.is_adversarial(predictions, 1)
assert not c.is_adversarial(predictions, 2)
assert c.is_adversarial(predictions, 3)
def test_misclassification_names():
c = criteria.Misclassification()
c1 = criteria.TopKMisclassification(k=1)
c5 = criteria.TopKMisclassification(k=5)
assert c.name() == c1.name()
assert c1.name() != c5.name()
c22 = criteria.TopKMisclassification(k=22)
assert "22" in c22.name()
def test_combined_criteria():
c1 = criteria.Misclassification()
c2 = criteria.OriginalClassProbability(0.2)
c3 = c1 & c2
probabilities = np.array([0.09, 0.11, 0.39, 0.41])
predictions = np.log(probabilities)
for i in range(len(predictions)):
b1 = c1.is_adversarial(predictions, i)
b2 = c2.is_adversarial(predictions, i)
b3 = c3.is_adversarial(predictions, i)
assert (b1 and b2) == b3
assert c1.name() == "Top1Misclassification"
assert c2.name() == "OriginalClassProbability-0.2000"
assert c3.name() == c2.name() + "__" + c1.name()
def cwAttack(victim_model): # victim_model should be model wrapped with foolbox model
attacker = attacks.CarliniWagnerL2Attack(victim_model, crt.Misclassification())
return FoolboxAttackWrapper(attacker)
def dfAttack(victim_model): # victim_model should be model wrapped with foolbox model
attacker = attacks.DeepFoolAttack(victim_model, crt.Misclassification())
return FoolboxAttackWrapper(attacker)
def pgdAttack(victim_model): # victim_model should be model wrapped with foolbox model
attacker = attacks.RandomStartProjectedGradientDescentAttack(victim_model, crt.Misclassification(), distance=distances.Linfinity)
return FoolboxAttackWrapper(attacker)
def fgsmAttack(victim_model): # victim_model should be model wrapped with foolbox model
attacker = attacks.GradientSignAttack(victim_model, crt.Misclassification())
return FoolboxAttackWrapper(attacker)
def Configuration(model,
criterion=criteria.Misclassification(),
distance=distances.MeanSquaredDistance):
return (model, criterion, distance)