def _run_threshold_entropy_attack(attack_input: AttackInputData):
ntrain, ntest = attack_input.get_train_size(), attack_input.get_test_size()
fpr, tpr, thresholds = metrics.roc_curve(
np.concatenate((np.zeros(ntrain), np.ones(ntest))),
np.concatenate((attack_input.get_entropy_train(),
attack_input.get_entropy_test())))
roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds)
return SingleAttackResult(
slice_spec=_get_slice_spec(attack_input),
data_size=DataSize(ntrain=ntrain, ntest=ntest),
attack_type=AttackType.THRESHOLD_ENTROPY_ATTACK,
membership_scores_train=-attack_input.get_entropy_train(),
membership_scores_test=-attack_input.get_entropy_test(),
roc_curve=roc_curve)
def test_get_entropy_explicitly_provided(self):
attack_input = AttackInputData(entropy_train=np.array([0.0, 2.0, 1.0]),
entropy_test=np.array([0.5, 3.0, 5.0]))
np.testing.assert_equal(attack_input.get_entropy_train().tolist(),
[0.0, 2.0, 1.0])
np.testing.assert_equal(attack_input.get_entropy_test().tolist(),
[0.5, 3.0, 5.0])
def test_get_entropy(self):
attack_input = AttackInputData(
logits_train=np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]),
logits_test=np.array([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
labels_train=np.array([0, 2]),
labels_test=np.array([0, 2]))
np.testing.assert_equal(attack_input.get_entropy_train().tolist(), [0, 0])
np.testing.assert_equal(attack_input.get_entropy_test().tolist(),
[2 * _log_value(0), 0])
attack_input = AttackInputData(
logits_train=np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]),
logits_test=np.array([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))
np.testing.assert_equal(attack_input.get_entropy_train().tolist(), [0, 0])
np.testing.assert_equal(attack_input.get_entropy_test().tolist(), [0, 0])
