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cw_attacks.py
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## l0_attack.py + l2_attack.py + li_attack.py-- attack a network optimizing for l_0, l_2 or l_infinity distance
## This is just a copy and paste from https://github.com/carlini/nn_robust_attacks.
## TODO: merge the code?
##
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import sys
import tensorflow.compat.v1 as tf
import numpy as np
from tqdm import tqdm
from cleverhans.utils import other_classes
import keras.backend as K
from util import lid_adv_term
# settings for C&W L2 attack
L2_BINARY_SEARCH_STEPS = 9 # number of times to adjust the constant with binary search
L2_MAX_ITERATIONS = 1000 # number of iterations to perform gradient descent
L2_ABORT_EARLY = True # if we stop improving, abort gradient descent early
L2_LEARNING_RATE = 1e-2 # larger values converge faster to less accurate results
L2_TARGETED = True # should we target one specific class? or just be wrong?
L2_CONFIDENCE = 0 # how strong the adversarial example should be
L2_INITIAL_CONST = 1e-3 # the initial constant c to pick as a first guess
class CarliniL2:
def __init__(self, sess, model, image_size, num_channels, num_labels, batch_size=100,
confidence=L2_CONFIDENCE, targeted=L2_TARGETED, learning_rate=L2_LEARNING_RATE,
binary_search_steps=L2_BINARY_SEARCH_STEPS, max_iterations=L2_MAX_ITERATIONS,
abort_early=L2_ABORT_EARLY,
initial_const=L2_INITIAL_CONST):
"""
The L_2 optimized attack.
This attack is the most efficient and should be used as the primary
attack to evaluate potential defenses.
Returns adversarial examples for the supplied model.
confidence: Confidence of adversarial examples: higher produces examples
that are farther away, but more strongly classified as adversarial.
batch_size: Number of attacks to run simultaneously.
targeted: True if we should perform a targetted attack, False otherwise.
learning_rate: The learning rate for the attack algorithm. Smaller values
produce better results but are slower to converge.
binary_search_steps: The number of times we perform binary search to
find the optimal tradeoff-constant between distance and confidence.
max_iterations: The maximum number of iterations. Larger values are more
accurate; setting too small will require a large learning rate and will
produce poor results.
abort_early: If true, allows early aborts if gradient descent gets stuck.
initial_const: The initial tradeoff-constant to use to tune the relative
importance of distance and confidence. If binary_search_steps is large,
the initial constant is not important.
"""
self.model = model
self.sess = sess
self.image_size = image_size
self.num_channels = num_channels
self.num_labels = num_labels
self.TARGETED = targeted
self.LEARNING_RATE = learning_rate
self.MAX_ITERATIONS = max_iterations
self.BINARY_SEARCH_STEPS = binary_search_steps
self.ABORT_EARLY = abort_early
self.CONFIDENCE = confidence
self.initial_const = initial_const
self.batch_size = batch_size
self.repeat = binary_search_steps >= 10
shape = (self.batch_size, self.image_size, self.image_size, self.num_channels)
# the variable we're going to optimize over
modifier = tf.Variable(np.zeros(shape, dtype=np.float32))
self.max_mod = tf.reduce_max(modifier)
# these are variables to be more efficient in sending data to tf
self.timg = tf.Variable(np.zeros(shape), dtype=tf.float32)
self.tlab = tf.Variable(np.zeros((self.batch_size, self.num_labels)), dtype=tf.float32)
self.const = tf.Variable(np.zeros(self.batch_size), dtype=tf.float32)
# and here's what we use to assign them
self.assign_timg = tf.placeholder(tf.float32, shape)
self.assign_tlab = tf.placeholder(tf.float32, (self.batch_size, self.num_labels))
self.assign_const = tf.placeholder(tf.float32, [self.batch_size])
# the resulting image, tanh'd to keep bounded from -0.5 to 0.5
self.newimg = tf.tanh(modifier + self.timg) / 2
# prediction BEFORE-SOFTMAX of the model
self.output = self.model(self.newimg)
# distance to the input data
self.l2dist = tf.reduce_sum(tf.square(self.newimg - tf.tanh(self.timg) / 2), [1, 2, 3])
# compute the probability of the label class versus the maximum other
real = tf.reduce_sum((self.tlab) * self.output, 1)
other = tf.reduce_max((1 - self.tlab) * self.output - (self.tlab * 10000), 1)
if self.TARGETED:
# if targetted, optimize for making the other class most likely
loss1 = tf.maximum(0.0, other - real + self.CONFIDENCE)
else:
# if untargeted, optimize for making this class least likely.
loss1 = tf.maximum(0.0, real - other + self.CONFIDENCE)
# sum up the losses
self.loss2 = tf.reduce_sum(self.l2dist)
self.loss1 = tf.reduce_sum(self.const * loss1)
self.loss = self.loss1 + self.loss2
self.grads = tf.reduce_max(tf.gradients(self.loss, [modifier]))
# Setup the adam optimizer and keep track of variables we're creating
start_vars = set(x.name for x in tf.global_variables())
optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE)
self.train = optimizer.minimize(self.loss, var_list=[modifier])
end_vars = tf.global_variables()
new_vars = [x for x in end_vars if x.name not in start_vars]
# these are the variables to initialize when we run
self.setup = []
self.setup.append(self.timg.assign(self.assign_timg))
self.setup.append(self.tlab.assign(self.assign_tlab))
self.setup.append(self.const.assign(self.assign_const))
self.init = tf.variables_initializer(var_list=[modifier] + new_vars)
def attack(self, X, Y):
"""
Perform the L_2 attack on the given images for the given targets.
:param X: samples to generate advs
:param Y: the original class labels
If self.targeted is true, then the targets represents the target labels.
If self.targeted is false, then targets are the original class labels.
"""
nb_classes = Y.shape[1]
# random select target class for targeted attack
y_target = np.copy(Y)
if self.TARGETED:
for i in range(Y.shape[0]):
current = int(np.argmax(Y[i]))
target = np.random.choice(other_classes(nb_classes, current))
y_target[i] = np.eye(nb_classes)[target]
X_adv = np.zeros_like(X)
for i in tqdm(range(0, X.shape[0], self.batch_size)):
start = i
end = i + self.batch_size
end = np.minimum(end, X.shape[0])
X_adv[start:end] = self.attack_batch(X[start:end], y_target[start:end])
return X_adv
def attack_batch(self, imgs, labs):
"""
Run the attack on a batch of images and labels.
"""
def compare(x, y):
if not isinstance(x, (float, int, np.int64)):
x = np.copy(x)
x[y] -= self.CONFIDENCE
x = np.argmax(x)
if self.TARGETED:
return x == y
else:
return x != y
# batch_size = self.batch_size
batch_size = imgs.shape[0]
# convert to tanh-space
imgs = np.arctanh(imgs * 1.999999)
# set the lower and upper bounds accordingly
lower_bound = np.zeros(batch_size)
CONST = np.ones(batch_size) * self.initial_const
upper_bound = np.ones(batch_size) * 1e10
# the best l2, score, and image attack
o_bestl2 = [1e10] * batch_size
o_bestscore = [-1] * batch_size
o_bestattack = [np.zeros(imgs[0].shape)] * batch_size
# o_bestattack = np.copy(imgs)
for outer_step in range(self.BINARY_SEARCH_STEPS):
# print(o_bestl2)
# completely reset adam's internal state.
self.sess.run(self.init)
batch = imgs[:batch_size]
batchlab = labs[:batch_size]
bestl2 = [1e10] * batch_size
bestscore = [-1] * batch_size
# The last iteration (if we run many steps) repeat the search once.
if self.repeat == True and outer_step == self.BINARY_SEARCH_STEPS - 1:
CONST = upper_bound
# set the variables so that we don't have to send them over again
self.sess.run(self.setup, {self.assign_timg: batch,
self.assign_tlab: batchlab,
self.assign_const: CONST})
prev = 1e6
for iteration in range(self.MAX_ITERATIONS):
# perform the attack
_, l, l2s, scores, nimg = self.sess.run([self.train, self.loss,
self.l2dist, self.output,
self.newimg], feed_dict={K.learning_phase(): 0})
# print out the losses every 10%
# if iteration % (self.MAX_ITERATIONS // 10) == 0:
# print(iteration, self.sess.run((self.loss, self.loss1, self.loss2, self.grads, self.max_mod), feed_dict={K.learning_phase(): 0}))
# check if we should abort search if we're getting nowhere.
if self.ABORT_EARLY and iteration % (self.MAX_ITERATIONS // 10) == 0:
if l > prev * .9999:
break
prev = l
# adjust the best result found so far
for e, (l2, sc, ii) in enumerate(zip(l2s, scores, nimg)):
if l2 < bestl2[e] and compare(sc, np.argmax(batchlab[e])):
bestl2[e] = l2
bestscore[e] = np.argmax(sc)
if l2 < o_bestl2[e] and compare(sc, np.argmax(batchlab[e])):
# print('l2:', l2, 'bestl2[e]: ', bestl2[e])
# print('score:', np.argmax(sc), 'bestscore[e]:', bestscore[e])
# print('np.argmax(batchlab[e]):', np.argmax(batchlab[e]))
o_bestl2[e] = l2
o_bestscore[e] = np.argmax(sc)
o_bestattack[e] = ii
# adjust the constant as needed
for e in range(batch_size):
if compare(bestscore[e], np.argmax(batchlab[e])) and bestscore[e] != -1:
# success, divide const by two
upper_bound[e] = min(upper_bound[e], CONST[e])
if upper_bound[e] < 1e9:
CONST[e] = (lower_bound[e] + upper_bound[e]) / 2
else:
# failure, either multiply by 10 if no solution found yet
# or do binary search with the known upper bound
lower_bound[e] = max(lower_bound[e], CONST[e])
if upper_bound[e] < 1e9:
CONST[e] = (lower_bound[e] + upper_bound[e]) / 2
else:
CONST[e] *= 10
# return the best solution found
o_bestl2 = np.array(o_bestl2)
print('sucess rate: %.4f' % (1-np.sum(o_bestl2==1e10)/self.batch_size))
return o_bestattack
class CarliniLID:
def __init__(self, sess, model, image_size, num_channels, num_labels, batch_size=100,
confidence=L2_CONFIDENCE, targeted=L2_TARGETED, learning_rate=L2_LEARNING_RATE,
binary_search_steps=L2_BINARY_SEARCH_STEPS, max_iterations=L2_MAX_ITERATIONS,
abort_early=L2_ABORT_EARLY,
initial_const=L2_INITIAL_CONST):
"""
The modified L_2 optimized attack to break LID detector.
This attack is the most efficient and should be used as the primary
attack to evaluate potential defenses.
Returns adversarial examples for the supplied model.
confidence: Confidence of adversarial examples: higher produces examples
that are farther away, but more strongly classified as adversarial.
batch_size: Number of attacks to run simultaneously.
targeted: True if we should perform a targetted attack, False otherwise.
learning_rate: The learning rate for the attack algorithm. Smaller values
produce better results but are slower to converge.
binary_search_steps: The number of times we perform binary search to
find the optimal tradeoff-constant between distance and confidence.
max_iterations: The maximum number of iterations. Larger values are more
accurate; setting too small will require a large learning rate and will
produce poor results.
abort_early: If true, allows early aborts if gradient descent gets stuck.
initial_const: The initial tradeoff-constant to use to tune the relative
importance of distance and confidence. If binary_search_steps is large,
the initial constant is not important.
"""
self.model = model
self.sess = sess
self.image_size = image_size
self.num_channels = num_channels
self.num_labels = num_labels
self.TARGETED = targeted
self.LEARNING_RATE = learning_rate
self.MAX_ITERATIONS = max_iterations
self.BINARY_SEARCH_STEPS = binary_search_steps
self.ABORT_EARLY = abort_early
self.CONFIDENCE = confidence
self.initial_const = initial_const
self.batch_size = batch_size
self.repeat = binary_search_steps >= 10
shape = (self.batch_size, self.image_size, self.image_size, self.num_channels)
# the variable we're going to optimize over
modifier = tf.Variable(np.zeros(shape, dtype=np.float32))
self.max_mod = tf.reduce_max(modifier)
# these are variables to be more efficient in sending data to tf
self.timg = tf.Variable(np.zeros(shape), dtype=tf.float32)
self.tlab = tf.Variable(np.zeros((self.batch_size, self.num_labels)), dtype=tf.float32)
self.const = tf.Variable(np.zeros(self.batch_size), dtype=tf.float32)
# and here's what we use to assign them
self.assign_timg = tf.placeholder(tf.float32, shape)
self.assign_tlab = tf.placeholder(tf.float32, (self.batch_size, self.num_labels))
self.assign_const = tf.placeholder(tf.float32, [self.batch_size])
# the resulting image, tanh'd to keep bounded from -0.5 to 0.5
self.newimg = tf.tanh(modifier + self.timg) / 2
# prediction BEFORE-SOFTMAX of the model
self.output = self.model(self.newimg)
# distance to the input data
self.l2dist = tf.reduce_sum(tf.square(self.newimg - tf.tanh(self.timg) / 2), [1, 2, 3])
# compute the probability of the label class versus the maximum other
real = tf.reduce_sum((self.tlab) * self.output, 1)
other = tf.reduce_max((1 - self.tlab) * self.output - (self.tlab * 10000), 1)
if self.TARGETED:
# if targetted, optimize for making the other class most likely
loss1 = tf.maximum(0.0, other - real + self.CONFIDENCE)
else:
# if untargeted, optimize for making this class least likely.
loss1 = tf.maximum(0.0, real - other + self.CONFIDENCE)
# add lis loss to the attack
self.clean_logits = tf.placeholder(tf.float32, (1, self.batch_size, None))
loss_lid = lid_adv_term(self.clean_logits, self.output, self.batch_size)
# sum up the losses
self.loss2 = tf.reduce_sum(self.l2dist)
self.loss1 = tf.reduce_sum(self.const * (loss1 + loss_lid))
self.loss = self.loss1 + self.loss2
self.grads = tf.reduce_max(tf.gradients(self.loss, [modifier]))
# Setup the adam optimizer and keep track of variables we're creating
start_vars = set(x.name for x in tf.global_variables())
optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE)
self.train = optimizer.minimize(self.loss, var_list=[modifier])
end_vars = tf.global_variables()
new_vars = [x for x in end_vars if x.name not in start_vars]
# these are the variables to initialize when we run
self.setup = []
self.setup.append(self.timg.assign(self.assign_timg))
self.setup.append(self.tlab.assign(self.assign_tlab))
self.setup.append(self.const.assign(self.assign_const))
self.init = tf.variables_initializer(var_list=[modifier] + new_vars)
def attack(self, X, Y):
"""
Perform the L_2 attack on the given images for the given targets.
:param X: samples to generate advs
:param Y: the original class labels
If self.targeted is true, then the targets represents the target labels.
If self.targeted is false, then targets are the original class labels.
"""
nb_classes = Y.shape[1]
# random select target class for targeted attack
y_target = np.copy(Y)
if self.TARGETED:
for i in range(Y.shape[0]):
current = int(np.argmax(Y[i]))
target = np.random.choice(other_classes(nb_classes, current))
y_target[i] = np.eye(nb_classes)[target]
X_adv = np.zeros_like(X)
for i in tqdm(range(0, X.shape[0], self.batch_size)):
start = i
end = i + self.batch_size
end = np.minimum(end, X.shape[0])
X_adv[start:end] = self.attack_batch(X[start:end], y_target[start:end])
return X_adv
def attack_batch(self, imgs, labs):
"""
Run the attack on a batch of images and labels.
"""
def compare(x, y):
if not isinstance(x, (float, int, np.int64)):
x = np.copy(x)
x[y] -= self.CONFIDENCE
x = np.argmax(x)
if self.TARGETED:
return x == y
else:
return x != y
# batch_size = self.batch_size
batch_size = imgs.shape[0]
# convert to tanh-space
imgs = np.arctanh(imgs * 1.999999)
# set the lower and upper bounds accordingly
lower_bound = np.zeros(batch_size)
CONST = np.ones(batch_size) * self.initial_const
upper_bound = np.ones(batch_size) * 1e10
# the best l2, score, and image attack
o_bestl2 = [1e10] * batch_size
o_bestscore = [-1] * batch_size
o_bestattack = [np.zeros(imgs[0].shape)] * batch_size
# o_bestattack = np.copy(imgs)
for outer_step in range(self.BINARY_SEARCH_STEPS):
# print(o_bestl2)
# completely reset adam's internal state.
self.sess.run(self.init)
batch = imgs[:batch_size]
batchlab = labs[:batch_size]
bestl2 = [1e10] * batch_size
bestscore = [-1] * batch_size
# The last iteration (if we run many steps) repeat the search once.
if self.repeat == True and outer_step == self.BINARY_SEARCH_STEPS - 1:
CONST = upper_bound
# set the variables so that we don't have to send them over again
self.sess.run(self.setup, {self.assign_timg: batch,
self.assign_tlab: batchlab,
self.assign_const: CONST})
# get clean logits of clean samples:
c_logits = self.sess.run([self.output], feed_dict={K.learning_phase(): 0})
prev = 1e6
for iteration in range(self.MAX_ITERATIONS):
# perform the attack
_, l, l2s, scores, nimg = self.sess.run([self.train, self.loss,
self.l2dist, self.output,
self.newimg], feed_dict={K.learning_phase(): 0,
self.clean_logits: c_logits})
# print out the losses every 10%
# if iteration % (self.MAX_ITERATIONS // 10) == 0:
# print(iteration, self.sess.run((self.loss, self.loss1, self.loss2, self.grads, self.max_mod), feed_dict={K.learning_phase(): 0}))
# check if we should abort search if we're getting nowhere.
if self.ABORT_EARLY and iteration % (self.MAX_ITERATIONS // 10) == 0:
if l > prev * .9999:
break
prev = l
# adjust the best result found so far
for e, (l2, sc, ii) in enumerate(zip(l2s, scores, nimg)):
if l2 < bestl2[e] and compare(sc, np.argmax(batchlab[e])):
bestl2[e] = l2
bestscore[e] = np.argmax(sc)
if l2 < o_bestl2[e] and compare(sc, np.argmax(batchlab[e])):
# print('l2:', l2, 'bestl2[e]: ', bestl2[e])
# print('score:', np.argmax(sc), 'bestscore[e]:', bestscore[e])
# print('np.argmax(batchlab[e]):', np.argmax(batchlab[e]))
o_bestl2[e] = l2
o_bestscore[e] = np.argmax(sc)
o_bestattack[e] = ii
# adjust the constant as needed
for e in range(batch_size):
if compare(bestscore[e], np.argmax(batchlab[e])) and bestscore[e] != -1:
# success, divide const by two
upper_bound[e] = min(upper_bound[e], CONST[e])
if upper_bound[e] < 1e9:
CONST[e] = (lower_bound[e] + upper_bound[e]) / 2
else:
# failure, either multiply by 10 if no solution found yet
# or do binary search with the known upper bound
lower_bound[e] = max(lower_bound[e], CONST[e])
if upper_bound[e] < 1e9:
CONST[e] = (lower_bound[e] + upper_bound[e]) / 2
else:
CONST[e] *= 10
# return the best solution found
o_bestl2 = np.array(o_bestl2)
print('sucess rate: %.4f' % (1-np.sum(o_bestl2==1e10)/self.batch_size))
return o_bestattack