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pgd_attack.py
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pgd_attack.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
from numpy import linalg as LA
class LinfPGDAttack:
def __init__(self, model, epsilon, eps_iter, nb_iter, kappa=0, random_start=False,
loss_func='xent', clip_min=0.0, clip_max=1.0):
"""Attack parameter initialization. The attack performs k steps of
size a, while always staying within epsilon from the initial
point."""
self.model = model
self.epsilon = epsilon
self.eps_iter = eps_iter
self.nb_iter = nb_iter
self.kappa = kappa
self.rand = random_start
self.clip_min = clip_min
self.clip_max = clip_max
self.x_input = self.model.layers[0].input
logits = self.model.layers[-2].output
y_pred = tf.nn.softmax(logits)
self.y_true = tf.placeholder(tf.float32, shape=y_pred.get_shape().as_list())
if loss_func == 'xent':
self.loss = -tf.reduce_sum(self.y_true * tf.log(y_pred+1e-20), axis=1)
elif loss_func == 'cw':
correct_logit = tf.reduce_sum(self.y_true * logits, axis=1)
wrong_logit = tf.reduce_max((1 - self.y_true) * logits, axis=1)
self.loss = -tf.nn.relu(correct_logit - wrong_logit + kappa)
else:
print('Unknown loss function. Defaulting to cross-entropy')
self.loss = -tf.reduce_sum(self.y_true * tf.log(y_pred), axis=1)
self.grad = tf.gradients(self.loss, self.x_input)[0]
def perturb(self, sess, x_nat, y, batch_size, ep, cri):
"""Given a set of examples (x_nat, y), returns a set of adversarial
examples within epsilon of x_nat in l_infinity norm."""
if self.rand:
x = x_nat + np.random.uniform(-self.epsilon, self.epsilon, x_nat.shape)
else:
x = np.copy(x_nat)
nb_batch = len(x) // batch_size
# check if need one more batch
if nb_batch * batch_size < len(x):
nb_batch += 1
fosc_batch = np.array([])
for i in range(nb_batch):
start = i * batch_size
end = (i + 1) * batch_size
end = np.minimum(end, len(x))
batch_x = x[start:end]
batch_y = y[start:end]
original_batch_x = np.copy(batch_x)
step_size = self.eps_iter
for j in range(self.nb_iter):
if j == 0:
loss, grad = sess.run([self.loss, self.grad],
feed_dict={self.x_input: batch_x,
self.y_true: batch_y})
batch_x += step_size * np.sign(grad)
else:
batch_x += np.multiply(step_size * np.sign(grad),
np.repeat(control_indicator, 32*32*3).reshape(batch_x.shape))
batch_x = np.clip(batch_x, x_nat[start:end] - self.epsilon, x_nat[start:end] + self.epsilon)
batch_x = np.clip(batch_x, self.clip_min, self.clip_max) # ensure valid pixel range
loss, grad = sess.run([self.loss, self.grad],
feed_dict={self.x_input: batch_x,
self.y_true: batch_y})
## compute the FOSC criterion, the grad can be reused by next step perturbation.
grad_adv = np.copy(grad)
grad_flatten = grad_adv.reshape(batch_x.shape[0], -1)
grad_norm = LA.norm(grad_flatten, ord=1, axis=1).reshape(-1, 1)
diff = (batch_x - original_batch_x).reshape(batch_x.shape[0], -1)
fosc = np.copy(grad_norm)
for i in range(batch_x.shape[0]):
fosc[i] = - np.dot(grad_flatten[i], diff[i]) + self.epsilon * grad_norm[i]
control_indicator = np.copy(fosc)
control_indicator[control_indicator<=cri] = 0
control_indicator[control_indicator>cri] = 1
if j == 0:
fosc_batch = fosc
else:
fosc_batch = np.concatenate((fosc_batch, fosc), axis=1)
x[start:end] = batch_x[:]
return x, fosc_batch
class TestLinfPGDAttack:
def __init__(self, model, epsilon, eps_iter, nb_iter, kappa=0, random_start=False,
loss_func='xent', clip_min=0.0, clip_max=1.0):
"""Attack parameter initialization. The attack performs k steps of
size a, while always staying within epsilon from the initial
point."""
self.model = model
self.epsilon = epsilon
self.eps_iter = eps_iter
self.nb_iter = nb_iter
self.kappa = kappa
self.rand = random_start
self.clip_min = clip_min
self.clip_max = clip_max
self.x_input = self.model.layers[0].input
logits = self.model.layers[-2].output
y_pred = tf.nn.softmax(logits)
self.y_true = tf.placeholder(tf.float32, shape=y_pred.get_shape().as_list())
if loss_func == 'xent':
self.loss = -tf.reduce_sum(self.y_true * tf.log(y_pred), axis=1)
elif loss_func == 'cw':
correct_logit = tf.reduce_sum(self.y_true * logits, axis=1)
wrong_logit = tf.reduce_max((1 - self.y_true) * logits, axis=1)
self.loss = -tf.nn.relu(correct_logit - wrong_logit + kappa)
else:
print('Unknown loss function. Defaulting to cross-entropy')
self.loss = -tf.reduce_sum(self.y_true * tf.log(y_pred), axis=1)
self.grad = tf.gradients(self.loss, self.x_input)[0]
def perturb(self, sess, x_nat, y, batch_size):
"""Given a set of examples (x_nat, y), returns a set of adversarial
examples within epsilon of x_nat in l_infinity norm."""
if self.rand:
x = x_nat + np.random.uniform(-self.epsilon, self.epsilon, x_nat.shape)
else:
x = np.copy(x_nat)
nb_batch = len(x) // batch_size
# check if need one more batch
if nb_batch * batch_size < len(x):
nb_batch += 1
for i in range(nb_batch):
start = i * batch_size
end = (i + 1) * batch_size
end = np.minimum(end, len(x))
batch_x = x[start:end]
batch_y = y[start:end]
for j in range(self.nb_iter):
loss, grad = sess.run([self.loss, self.grad],
feed_dict={self.x_input: batch_x,
self.y_true: batch_y})
batch_x += self.eps_iter * np.sign(grad)
batch_x = np.clip(batch_x, x_nat[start:end] - self.epsilon, x_nat[start:end] + self.epsilon)
batch_x = np.clip(batch_x, self.clip_min, self.clip_max) # ensure valid pixel range
x[start:end] = batch_x[:]
return x