def _cnn_mnist_tf(input_shape):
labels_tf = tf.placeholder(tf.float32, [None, 10])
inputs_tf = tf.placeholder(tf.float32, [None] + list(input_shape))
# Define the tensorflow graph
conv = tf.layers.conv2d(inputs_tf, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
logits = tf.layers.dense(fc, 10)
# Train operator
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=labels_tf))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_tf = optimizer.minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
classifier = TFClassifier((0, 1),
inputs_tf,
logits,
loss=loss,
train=train_tf,
output_ph=labels_tf,
sess=sess)
return classifier
def test_tfclassifier(self):
"""
First test with the TFClassifier.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
self._input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
self._output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(self._input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
self._logits = tf.layers.dense(fc, 10)
# Train operator
self._loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=self._logits,
onehot_labels=self._output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
self._train = optimizer.minimize(self._loss)
# Tensorflow session and initialization
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
# Get MNIST
batch_size, nb_train, nb_test = 10, 10, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
x_train, y_train = x_train[:nb_train], y_train[:nb_train]
x_test, y_test = x_test[:nb_test], y_test[:nb_test]
# Train the classifier
tfc = TFClassifier((0, 1), self._input_ph, self._logits,
self._output_ph, self._train, self._loss, None,
self._sess)
tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
# Attack
# TODO Launch with all possible attacks
attack_params = {
"attacker": "newtonfool",
"attacker_params": {
"max_iter": 20
}
}
up = UniversalPerturbation(tfc)
x_train_adv = up.generate(x_train, **attack_params)
self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
x_test_adv = x_test + up.v
self.assertFalse((x_test == x_test_adv).all())
train_y_pred = np.argmax(tfc.predict(x_train_adv), axis=1)
test_y_pred = np.argmax(tfc.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(y_test, axis=1) == test_y_pred).all())
self.assertFalse((np.argmax(y_train, axis=1) == train_y_pred).all())
def _create_tfclassifier():
"""
To create a simple TFClassifier for testing.
:return:
"""
# Define input and output placeholders
input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
logits = tf.layers.dense(fc, 10)
# Train operator
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train = optimizer.minimize(loss)
# Tensorflow session and initialization
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Create the classifier
tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss,
None, sess)
return tfc
def test_failure_attack(self):
"""
Test the corner case when attack is failed.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
self._input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
self._output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(self._input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
self._logits = tf.layers.dense(fc, 10)
# Train operator
self._loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=self._logits,
onehot_labels=self._output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
self._train = optimizer.minimize(self._loss)
# Tensorflow session and initialization
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
# Get MNIST
batch_size, nb_train, nb_test = 100, 5000, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
x_train, y_train = x_train[:nb_train], y_train[:nb_train]
x_test, y_test = x_test[:nb_test], y_test[:nb_test]
# Train the classifier
tfc = TFClassifier((0, 1), self._input_ph, self._logits,
self._output_ph, self._train, self._loss, None,
self._sess)
tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=10)
# Failure attack
cl2m = CarliniL2Method(classifier=tfc,
targeted=True,
max_iter=0,
binary_search_steps=0,
learning_rate=0,
initial_const=1,
decay=0)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = cl2m.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
np.testing.assert_almost_equal(x_test, x_test_adv, 3)
def test_tfclassifier(self):
"""
First test with the TFClassifier.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
self._input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
self._output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(self._input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
self._logits = tf.layers.dense(fc, 10)
# Train operator
self._loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=self._logits,
onehot_labels=self._output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
self._train = optimizer.minimize(self._loss)
# Tensorflow session and initialization
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
# Get MNIST
batch_size, nb_train, nb_test = 100, 1000, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
x_train, y_train = x_train[:nb_train], y_train[:nb_train]
x_test, y_test = x_test[:nb_test], y_test[:nb_test]
# Train the classifier
tfc = TFClassifier((0, 1), self._input_ph, self._logits,
self._output_ph, self._train, self._loss, None,
self._sess)
tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
# Attack
nf = NewtonFool(tfc)
nf.set_params(max_iter=5)
x_test_adv = nf.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
y_pred = tfc.predict(x_test)
y_pred_adv = tfc.predict(x_test_adv)
y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred
y_pred_max = y_pred.max(axis=1)
y_pred_adv_max = y_pred_adv[y_pred_bool]
self.assertTrue((y_pred_max >= y_pred_adv_max).all())
def test_tfclassifier(self):
"""
First test with the TFClassifier.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
self._input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
self._output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(self._input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
self._logits = tf.layers.dense(fc, 10)
# Train operator
self._loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=self._logits,
onehot_labels=self._output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
self._train = optimizer.minimize(self._loss)
# Tensorflow session and initialization
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
# Get MNIST
batch_size, nb_train, nb_test = 100, 5000, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
x_train, y_train = x_train[:nb_train], y_train[:nb_train]
x_test, y_test = x_test[:nb_test], y_test[:nb_test]
# Train the classifier
tfc = TFClassifier((0, 1), self._input_ph, self._logits,
self._output_ph, self._train, self._loss, None,
self._sess)
tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=10)
# First attack
cl2m = CarliniL2Method(classifier=tfc,
targeted=True,
max_iter=100,
binary_search_steps=1,
learning_rate=1,
initial_const=10,
decay=0)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = cl2m.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
#print(x_test_adv)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
print("CW2 Target: %s" % target)
print("CW2 Actual: %s" % y_pred_adv)
print("CW2 Success Rate: %f" %
(sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
cl2m = CarliniL2Method(classifier=tfc,
targeted=False,
max_iter=100,
binary_search_steps=1,
learning_rate=1,
initial_const=10,
decay=0)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = cl2m.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
print("CW2 Target: %s" % target)
print("CW2 Actual: %s" % y_pred_adv)
print("CW2 Success Rate: %f" %
(sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Third attack
cl2m = CarliniL2Method(classifier=tfc,
targeted=False,
max_iter=100,
binary_search_steps=1,
learning_rate=1,
initial_const=10,
decay=0)
params = {}
x_test_adv = cl2m.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
y_pred = np.argmax(tfc.predict(x_test), axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
print("CW2 Target: %s" % y_pred)
print("CW2 Actual: %s" % y_pred_adv)
print("CW2 Success Rate: %f" %
(sum(y_pred != y_pred_adv) / float(len(y_pred))))
self.assertTrue((y_pred != y_pred_adv).any())