def build_graph(self, x, y):
with freeze_variables(stop_gradient=False, skip_collection=True):
step_size = FLAGS.step_size / 255.0
max_epsilon = FLAGS.max_epsilon / 255.0
x_max = tf.clip_by_value(x + max_epsilon, 0., 1.0)
x_min = tf.clip_by_value(x - max_epsilon, 0., 1.0)
logits, _, _ = network.model(x, FLAGS.attack_networks[0])
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=y)
noise = tf.gradients(loss, x)[0]
with tf.variable_scope('RHP'):
noise = conv_with_rn(noise)
with freeze_variables(stop_gradient=False, skip_collection=True):
G = tf.get_default_graph()
with G.gradient_override_map({"Sign": "Identity"}):
x = x + step_size * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
# evaluate after add perturbation
logits, _, _ = network.model(x, FLAGS.attack_networks[0])
loss_to_optimize = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=y),
name='train_loss')
return -loss_to_optimize
def build_graph(self):
batch_shape = [FLAGS.batch_size, 299, 299, 3]
self.x_input = tf.placeholder(tf.float32, shape=batch_shape)
self.y_input = tf.placeholder(tf.int64, shape=batch_shape[0])
self.acc_list = []
for network_name in FLAGS.test_networks:
acc = network.model(self.x_input, network_name, label=self.y_input)
self.acc_list.append(acc)
def build_graph(self):
batch_shape = [None, 299, 299, 3]
self.x_input = tf.placeholder(tf.float32, shape=batch_shape)
self.y_input = tf.placeholder(tf.int64, shape=batch_shape[0])
self.acc_list = []
self.predictions = []
with TowerContext("model_tower", is_training=False):
for network_name in FLAGS.test_networks:
acc, predictions = network.model(self.x_input, network_name, label=self.y_input)
self.acc_list.append(acc)
self.predictions.append(predictions)
def graph(self, x, y, i, x_max, x_min):
with TowerContext("model_tower", is_training=False):
logits, _, endpoints = network.model(x, FLAGS.attack_networks[0])
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=y)
noise = tf.gradients(loss,
x)[0] if not FLAGS.universal else tf.zeros_like(x)
with TowerContext('RHP_tower', is_training=False):
with tf.variable_scope('RHP'):
noise = conv_with_rn(noise)
noise = noise / (
tf.reduce_mean(tf.abs(noise), [1, 2, 3], keepdims=True) + 1e-12)
x = x + self.step_size * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min
def graph(self, x, y, i, x_max, x_min, grad):
logits_list = []
for network_name in FLAGS.attack_networks:
logits, _, endpoints = network.model(x, network_name)
logits_list.append(logits)
logits_mean = tf.reduce_mean(logits_list, axis=0)
# pred = tf.argmax(logits_mean, axis=1)
# first_round = tf.cast(tf.equal(i, 0), tf.int64)
# y = first_round * pred + (1 - first_round) * y
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits_mean, labels=y)
noise = tf.gradients(loss, x)[0]
noise = noise / (
tf.reduce_mean(tf.abs(noise), [1, 2, 3], keepdims=True) + 1e-12)
noise = FLAGS.momentum * grad + noise
x = x + self.step_size * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise