-
Notifications
You must be signed in to change notification settings - Fork 1.1k
/
carlini.py
775 lines (654 loc) · 41.8 KB
/
carlini.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
# MIT License
#
# Copyright (C) IBM Corporation 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
This module implements the L2 and LInf optimized attacks `CarliniL2Method` and `CarliniLInfMethod` of Carlini and Wagner
(2016). These attacks are among the most effective white-box attacks and should be used among the primary attacks to
evaluate potential defences. A major difference with respect to the original implementation
(https://github.com/carlini/nn_robust_attacks) is that this implementation uses line search in the optimization of the
attack objective.
| Paper link: https://arxiv.org/abs/1608.04644
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
from art import NUMPY_DTYPE
from art.classifiers.classifier import ClassifierGradients
from art.attacks.attack import Attack
from art.utils import compute_success, get_labels_np_array, tanh_to_original, original_to_tanh
from art.utils import check_and_transform_label_format
logger = logging.getLogger(__name__)
class CarliniL2Method(Attack):
"""
The L_2 optimized attack of Carlini and Wagner (2016). This attack is among the most effective and should be used
among the primary attacks to evaluate potential defences. A major difference wrt to the original implementation
(https://github.com/carlini/nn_robust_attacks) is that we use line search in the optimization of the attack
objective.
| Paper link: https://arxiv.org/abs/1608.04644
"""
attack_params = Attack.attack_params + ['confidence', 'targeted', 'learning_rate', 'max_iter',
'binary_search_steps', 'initial_const', 'max_halving', 'max_doubling',
'batch_size']
def __init__(self, classifier, confidence=0.0, targeted=False, learning_rate=0.01, binary_search_steps=10,
max_iter=10, initial_const=0.01, max_halving=5, max_doubling=5, batch_size=1):
"""
Create a Carlini L_2 attack instance.
:param classifier: A trained classifier.
:type classifier: :class:`.Classifier`
:param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
from the original input, but classified with higher confidence as the target class.
:type confidence: `float`
:param targeted: Should the attack target one specific class.
:type targeted: `bool`
:param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better results
but are slower to converge.
:type learning_rate: `float`
:param binary_search_steps: Number of times to adjust constant with binary search (positive value). If
`binary_search_steps` is large, then the algorithm is not very sensitive to the
value of `initial_const`. Note that the values gamma=0.999999 and c_upper=10e10 are
hardcoded with the same values used by the authors of the method.
:type binary_search_steps: `int`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param initial_const: The initial trade-off constant `c` to use to tune the relative importance of distance and
confidence. If `binary_search_steps` is large, the initial constant is not important, as discussed in
Carlini and Wagner (2016).
:type initial_const: `float`
:param max_halving: Maximum number of halving steps in the line search optimization.
:type max_halving: `int`
:param max_doubling: Maximum number of doubling steps in the line search optimization.
:type max_doubling: `int`
:param batch_size: Size of the batch on which adversarial samples are generated.
:type batch_size: `int`
"""
super(CarliniL2Method, self).__init__(classifier)
if not isinstance(classifier, ClassifierGradients):
raise (TypeError('For `' + self.__class__.__name__ + '` classifier must be an instance of '
'`art.classifiers.classifier.ClassifierGradients`, the provided classifier is instance of '
+ str(classifier.__class__.__bases__) + '. '
' The classifier needs to provide gradients.'))
kwargs = {'confidence': confidence,
'targeted': targeted,
'learning_rate': learning_rate,
'binary_search_steps': binary_search_steps,
'max_iter': max_iter,
'initial_const': initial_const,
'max_halving': max_halving,
'max_doubling': max_doubling,
'batch_size': batch_size
}
assert self.set_params(**kwargs)
# There are internal hyperparameters:
# Abort binary search for c if it exceeds this threshold (suggested in Carlini and Wagner (2016)):
self._c_upper_bound = 10e10
# Smooth arguments of arctanh by multiplying with this constant to avoid division by zero.
# It appears this is what Carlini and Wagner (2016) are alluding to in their footnote 8. However, it is not
# clear how their proposed trick ("instead of scaling by 1/2 we scale by 1/2 + eps") works in detail.
self._tanh_smoother = 0.999999
def _loss(self, x, x_adv, target, c_weight):
"""
Compute the objective function value.
:param x: An array with the original input.
:type x: `np.ndarray`
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:param c_weight: Weight of the loss term aiming for classification as target.
:type c_weight: `float`
:return: A tuple holding the current logits, l2 distance and overall loss.
:rtype: `(float, float, float)`
"""
l2dist = np.sum(np.square(x - x_adv).reshape(x.shape[0], -1), axis=1)
z_predicted = self.classifier.predict(np.array(x_adv, dtype=NUMPY_DTYPE), logits=True,
batch_size=self.batch_size)
z_target = np.sum(z_predicted * target, axis=1)
z_other = np.max(z_predicted * (1 - target) + (np.min(z_predicted, axis=1) - 1)[:, np.newaxis] * target, axis=1)
# The following differs from the exact definition given in Carlini and Wagner (2016). There (page 9, left
# column, last equation), the maximum is taken over Z_other - Z_target (or Z_target - Z_other respectively)
# and -confidence. However, it doesn't seem that that would have the desired effect (loss term is <= 0 if and
# only if the difference between the logit of the target and any other class differs by at least confidence).
# Hence the rearrangement here.
if self.targeted:
# if targeted, optimize for making the target class most likely
loss = np.maximum(z_other - z_target + self.confidence, np.zeros(x.shape[0]))
else:
# if untargeted, optimize for making any other class most likely
loss = np.maximum(z_target - z_other + self.confidence, np.zeros(x.shape[0]))
return z_predicted, l2dist, c_weight * loss + l2dist
def _loss_gradient(self, z_logits, target, x, x_adv, x_adv_tanh, c_weight, clip_min, clip_max):
"""
Compute the gradient of the loss function.
:param z_logits: An array with the current logits.
:type z_logits: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:param x: An array with the original input.
:type x: `np.ndarray`
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param x_adv_tanh: An array with the adversarial input in tanh space.
:type x_adv_tanh: `np.ndarray`
:param c_weight: Weight of the loss term aiming for classification as target.
:type c_weight: `float`
:param clip_min: Minimum clipping value.
:type clip_min: `float`
:param clip_max: Maximum clipping value.
:type clip_max: `float`
:return: An array with the gradient of the loss function.
:type target: `np.ndarray`
"""
if self.targeted:
i_sub = np.argmax(target, axis=1)
i_add = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1)
else:
i_add = np.argmax(target, axis=1)
i_sub = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1)
loss_gradient = self.classifier.class_gradient(x_adv, label=i_add)
loss_gradient -= self.classifier.class_gradient(x_adv, label=i_sub)
loss_gradient = loss_gradient.reshape(x.shape)
c_mult = c_weight
for _ in range(len(x.shape) - 1):
c_mult = c_mult[:, np.newaxis]
loss_gradient *= c_mult
loss_gradient += 2 * (x_adv - x)
loss_gradient *= (clip_max - clip_min)
loss_gradient *= (1 - np.square(np.tanh(x_adv_tanh))) / (2 * self._tanh_smoother)
return loss_gradient
def generate(self, x, y=None, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
(nb_samples,). If `self.targeted` is true, then `y` represents the target labels. If `self.targeted`
is true, then `y_val` represents the target labels. Otherwise, the targets are the original class
labels.
:return: An array holding the adversarial examples.
:rtype: `np.ndarray`
"""
y = check_and_transform_label_format(y, self.classifier.nb_classes())
x_adv = x.astype(NUMPY_DTYPE)
if hasattr(self.classifier, 'clip_values') and self.classifier.clip_values is not None:
clip_min, clip_max = self.classifier.clip_values
else:
clip_min, clip_max = np.amin(x), np.amax(x)
# Assert that, if attack is targeted, y_val is provided:
if self.targeted and y is None:
raise ValueError('Target labels `y` need to be provided for a targeted attack.')
# No labels provided, use model prediction as correct class
if y is None:
y = get_labels_np_array(self.classifier.predict(x, batch_size=self.batch_size))
# Compute perturbation with implicit batching
nb_batches = int(np.ceil(x_adv.shape[0] / float(self.batch_size)))
for batch_id in range(nb_batches):
logger.debug('Processing batch %i out of %i', batch_id, nb_batches)
batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
x_batch = x_adv[batch_index_1:batch_index_2]
y_batch = y[batch_index_1:batch_index_2]
# The optimization is performed in tanh space to keep the adversarial images bounded in correct range
x_batch_tanh = original_to_tanh(x_batch, clip_min, clip_max, self._tanh_smoother)
# Initialize binary search:
c_current = self.initial_const * np.ones(x_batch.shape[0])
c_lower_bound = np.zeros(x_batch.shape[0])
c_double = (np.ones(x_batch.shape[0]) > 0)
# Initialize placeholders for best l2 distance and attack found so far
best_l2dist = np.inf * np.ones(x_batch.shape[0])
best_x_adv_batch = x_batch.copy()
for bss in range(self.binary_search_steps):
logger.debug('Binary search step %i out of %i (c_mean==%f)', bss, self.binary_search_steps,
np.mean(c_current))
nb_active = int(np.sum(c_current < self._c_upper_bound))
logger.debug('Number of samples with c_current < _c_upper_bound: %i out of %i', nb_active,
x_batch.shape[0])
if nb_active == 0:
break
learning_rate = self.learning_rate * np.ones(x_batch.shape[0])
# Initialize perturbation in tanh space:
x_adv_batch = x_batch.copy()
x_adv_batch_tanh = x_batch_tanh.copy()
z_logits, l2dist, loss = self._loss(x_batch, x_adv_batch, y_batch, c_current)
attack_success = (loss - l2dist <= 0)
overall_attack_success = attack_success
for i_iter in range(self.max_iter):
logger.debug('Iteration step %i out of %i', i_iter, self.max_iter)
logger.debug('Average Loss: %f', np.mean(loss))
logger.debug('Average L2Dist: %f', np.mean(l2dist))
logger.debug('Average Margin Loss: %f', np.mean(loss - l2dist))
logger.debug('Current number of succeeded attacks: %i out of %i', int(np.sum(attack_success)),
len(attack_success))
improved_adv = attack_success & (l2dist < best_l2dist)
logger.debug('Number of improved L2 distances: %i', int(np.sum(improved_adv)))
if np.sum(improved_adv) > 0:
best_l2dist[improved_adv] = l2dist[improved_adv]
best_x_adv_batch[improved_adv] = x_adv_batch[improved_adv]
active = (c_current < self._c_upper_bound) & (learning_rate > 0)
nb_active = int(np.sum(active))
logger.debug(
'Number of samples with c_current < _c_upper_bound and learning_rate > 0: %i out of %i',
nb_active, x_batch.shape[0])
if nb_active == 0:
break
# compute gradient:
logger.debug('Compute loss gradient')
perturbation_tanh = -self._loss_gradient(z_logits[active], y_batch[active], x_batch[active],
x_adv_batch[active], x_adv_batch_tanh[active],
c_current[active], clip_min, clip_max)
# perform line search to optimize perturbation
# first, halve the learning rate until perturbation actually decreases the loss:
prev_loss = loss.copy()
best_loss = loss.copy()
best_lr = np.zeros(x_batch.shape[0])
halving = np.zeros(x_batch.shape[0])
for i_halve in range(self.max_halving):
logger.debug('Perform halving iteration %i out of %i', i_halve, self.max_halving)
do_halving = (loss[active] >= prev_loss[active])
logger.debug('Halving to be performed on %i samples', int(np.sum(do_halving)))
if np.sum(do_halving) == 0:
break
active_and_do_halving = active.copy()
active_and_do_halving[active] = do_halving
lr_mult = learning_rate[active_and_do_halving]
for _ in range(len(x.shape) - 1):
lr_mult = lr_mult[:, np.newaxis]
new_x_adv_batch_tanh = x_adv_batch_tanh[active_and_do_halving] + lr_mult * perturbation_tanh[
do_halving]
new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh, clip_min, clip_max,
self._tanh_smoother)
_, l2dist[active_and_do_halving], loss[active_and_do_halving] = self._loss(
x_batch[active_and_do_halving], new_x_adv_batch, y_batch[active_and_do_halving],
c_current[active_and_do_halving])
logger.debug('New Average Loss: %f', np.mean(loss))
logger.debug('New Average L2Dist: %f', np.mean(l2dist))
logger.debug('New Average Margin Loss: %f', np.mean(loss - l2dist))
best_lr[loss < best_loss] = learning_rate[loss < best_loss]
best_loss[loss < best_loss] = loss[loss < best_loss]
learning_rate[active_and_do_halving] /= 2
halving[active_and_do_halving] += 1
learning_rate[active] *= 2
# if no halving was actually required, double the learning rate as long as this
# decreases the loss:
for i_double in range(self.max_doubling):
logger.debug('Perform doubling iteration %i out of %i', i_double, self.max_doubling)
do_doubling = (halving[active] == 1) & (loss[active] <= best_loss[active])
logger.debug('Doubling to be performed on %i samples', int(np.sum(do_doubling)))
if np.sum(do_doubling) == 0:
break
active_and_do_doubling = active.copy()
active_and_do_doubling[active] = do_doubling
learning_rate[active_and_do_doubling] *= 2
lr_mult = learning_rate[active_and_do_doubling]
for _ in range(len(x.shape) - 1):
lr_mult = lr_mult[:, np.newaxis]
new_x_adv_batch_tanh = x_adv_batch_tanh[active_and_do_doubling] + lr_mult * perturbation_tanh[
do_doubling]
new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh, clip_min, clip_max,
self._tanh_smoother)
_, l2dist[active_and_do_doubling], loss[active_and_do_doubling] = self._loss(
x_batch[active_and_do_doubling], new_x_adv_batch, y_batch[active_and_do_doubling],
c_current[active_and_do_doubling])
logger.debug('New Average Loss: %f', np.mean(loss))
logger.debug('New Average L2Dist: %f', np.mean(l2dist))
logger.debug('New Average Margin Loss: %f', np.mean(loss - l2dist))
best_lr[loss < best_loss] = learning_rate[loss < best_loss]
best_loss[loss < best_loss] = loss[loss < best_loss]
learning_rate[halving == 1] /= 2
update_adv = (best_lr[active] > 0)
logger.debug('Number of adversarial samples to be finally updated: %i', int(np.sum(update_adv)))
if np.sum(update_adv) > 0:
active_and_update_adv = active.copy()
active_and_update_adv[active] = update_adv
best_lr_mult = best_lr[active_and_update_adv]
for _ in range(len(x.shape) - 1):
best_lr_mult = best_lr_mult[:, np.newaxis]
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[
active_and_update_adv] + best_lr_mult * perturbation_tanh[update_adv]
x_adv_batch[active_and_update_adv] = tanh_to_original(x_adv_batch_tanh[active_and_update_adv],
clip_min, clip_max, self._tanh_smoother)
z_logits[active_and_update_adv], l2dist[active_and_update_adv], loss[active_and_update_adv] = \
self._loss(x_batch[active_and_update_adv], x_adv_batch[active_and_update_adv],
y_batch[active_and_update_adv], c_current[active_and_update_adv])
attack_success = (loss - l2dist <= 0)
overall_attack_success = overall_attack_success | attack_success
# Update depending on attack success:
improved_adv = attack_success & (l2dist < best_l2dist)
logger.debug('Number of improved L2 distances: %i', int(np.sum(improved_adv)))
if np.sum(improved_adv) > 0:
best_l2dist[improved_adv] = l2dist[improved_adv]
best_x_adv_batch[improved_adv] = x_adv_batch[improved_adv]
c_double[overall_attack_success] = False
c_current[overall_attack_success] = (c_lower_bound + c_current)[overall_attack_success] / 2
c_old = c_current
c_current[~overall_attack_success & c_double] *= 2
c_current[~overall_attack_success & ~c_double] += (c_current - c_lower_bound)[
~overall_attack_success & ~c_double] / 2
c_lower_bound[~overall_attack_success] = c_old[~overall_attack_success]
x_adv[batch_index_1:batch_index_2] = best_x_adv_batch
logger.info('Success rate of C&W L_2 attack: %.2f%%',
100 * compute_success(self.classifier, x, y, x_adv, self.targeted, batch_size=self.batch_size))
return x_adv
def set_params(self, **kwargs):
"""Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
:param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
from the original input, but classified with higher confidence as the target class.
:type confidence: `float`
:param targeted: Should the attack target one specific class
:type targeted: `bool`
:param learning_rate: The learning rate for the attack algorithm. Smaller values produce better results but are
slower to converge.
:type learning_rate: `float`
:param binary_search_steps: number of times to adjust constant with binary search (positive value)
:type binary_search_steps: `int`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param initial_const: (optional float, positive) The initial trade-off constant c to use to tune the relative
importance of distance and confidence. If binary_search_steps is large,
the initial constant is not important. The default value 1e-4 is suggested in Carlini and Wagner (2016).
:type initial_const: `float`
:param max_halving: Maximum number of halving steps in the line search optimization.
:type max_halving: `int`
:param max_doubling: Maximum number of doubling steps in the line search optimization.
:type max_doubling: `int`
:param batch_size: Internal size of batches on which adversarial samples are generated.
:type batch_size: `int`
"""
# Save attack-specific parameters
super(CarliniL2Method, self).set_params(**kwargs)
if not isinstance(self.binary_search_steps, (int, np.int)) or self.binary_search_steps < 0:
raise ValueError("The number of binary search steps must be a non-negative integer.")
if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
raise ValueError("The number of iterations must be a non-negative integer.")
if not isinstance(self.max_halving, (int, np.int)) or self.max_halving < 1:
raise ValueError("The number of halving steps must be an integer greater than zero.")
if not isinstance(self.max_doubling, (int, np.int)) or self.max_doubling < 1:
raise ValueError("The number of doubling steps must be an integer greater than zero.")
if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 1:
raise ValueError("The batch size must be an integer greater than zero.")
return True
class CarliniLInfMethod(Attack):
"""
This is a modified version of the L_2 optimized attack of Carlini and Wagner (2016). It controls the L_Inf
norm, i.e. the maximum perturbation applied to each pixel.
"""
attack_params = Attack.attack_params + ['confidence', 'targeted', 'learning_rate', 'max_iter',
'max_halving', 'max_doubling', 'eps', 'batch_size']
def __init__(self, classifier, confidence=0.0, targeted=False, learning_rate=0.01, max_iter=10, max_halving=5,
max_doubling=5, eps=0.3, batch_size=128):
"""
Create a Carlini L_Inf attack instance.
:param classifier: A trained classifier.
:type classifier: :class:`.Classifier`
:param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
from the original input, but classified with higher confidence as the target class.
:type confidence: `float`
:param targeted: Should the attack target one specific class.
:type targeted: `bool`
:param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better
results but are slower to converge.
:type learning_rate: `float`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param max_halving: Maximum number of halving steps in the line search optimization.
:type max_halving: `int`
:param max_doubling: Maximum number of doubling steps in the line search optimization.
:type max_doubling: `int`
:param eps: An upper bound for the L_0 norm of the adversarial perturbation.
:type eps: `float`
:param batch_size: Size of the batch on which adversarial samples are generated.
:type batch_size: `int`
:param expectation: An expectation over transformations to be applied when computing
classifier gradients and predictions.
:type expectation: :class:`.ExpectationOverTransformations`
"""
super(CarliniLInfMethod, self).__init__(classifier)
if not isinstance(classifier, ClassifierGradients):
raise (TypeError('For `' + self.__class__.__name__ + '` classifier must be an instance of '
'`art.classifiers.classifier.ClassifierGradients`, the provided classifier is instance of '
+ str(classifier.__class__.__bases__) + '. '
' The classifier needs to provide gradients.'))
kwargs = {'confidence': confidence,
'targeted': targeted,
'learning_rate': learning_rate,
'max_iter': max_iter,
'max_halving': max_halving,
'max_doubling': max_doubling,
'eps': eps,
'batch_size': batch_size
}
assert self.set_params(**kwargs)
# There is one internal hyperparameter:
# Smooth arguments of arctanh by multiplying with this constant to avoid division by zero:
self._tanh_smoother = 0.999999
def _loss(self, x_adv, target):
"""
Compute the objective function value.
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:return: A tuple holding the current predictions and overall loss.
:rtype: `(float, float)`
"""
z_predicted = self.classifier.predict(np.array(x_adv, dtype=NUMPY_DTYPE), batch_size=self.batch_size)
z_target = np.sum(z_predicted * target, axis=1)
z_other = np.max(z_predicted * (1 - target) + (np.min(z_predicted, axis=1) - 1)[:, np.newaxis] * target, axis=1)
if self.targeted:
# if targeted, optimize for making the target class most likely
loss = np.maximum(z_other - z_target + self.confidence, np.zeros(x_adv.shape[0]))
else:
# if untargeted, optimize for making any other class most likely
loss = np.maximum(z_target - z_other + self.confidence, np.zeros(x_adv.shape[0]))
return z_predicted, loss
def _loss_gradient(self, z_logits, target, x_adv, x_adv_tanh, clip_min, clip_max): # lgtm [py/similar-function]
"""
Compute the gradient of the loss function.
:param z_logits: An array with the current predictions.
:type z_logits: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param x_adv_tanh: An array with the adversarial input in tanh space.
:type x_adv_tanh: `np.ndarray`
:param clip_min: Minimum clipping values.
:type clip_min: `np.ndarray`
:param clip_max: Maximum clipping values.
:type clip_max: `np.ndarray`
:return: An array with the gradient of the loss function.
:type target: `np.ndarray`
"""
if self.targeted:
i_sub = np.argmax(target, axis=1)
i_add = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1)
else:
i_add = np.argmax(target, axis=1)
i_sub = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1)
loss_gradient = self.classifier.class_gradient(x_adv, label=i_add)
loss_gradient -= self.classifier.class_gradient(x_adv, label=i_sub)
loss_gradient = loss_gradient.reshape(x_adv.shape)
loss_gradient *= (clip_max - clip_min)
loss_gradient *= (1 - np.square(np.tanh(x_adv_tanh))) / (2 * self._tanh_smoother)
return loss_gradient
def generate(self, x, y=None, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
(nb_samples,). If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the
targets are the original class labels.
:type y: `np.ndarray`
:return: An array holding the adversarial examples.
:rtype: `np.ndarray`
"""
y = check_and_transform_label_format(y, self.classifier.nb_classes())
x_adv = x.astype(NUMPY_DTYPE)
if hasattr(self.classifier, 'clip_values') and self.classifier.clip_values is not None:
clip_min_per_pixel, clip_max_per_pixel = self.classifier.clip_values
else:
clip_min_per_pixel, clip_max_per_pixel = np.amin(x), np.amax(x)
# Assert that, if attack is targeted, y_val is provided:
if self.targeted and y is None:
raise ValueError('Target labels `y` need to be provided for a targeted attack.')
# No labels provided, use model prediction as correct class
if y is None:
y = get_labels_np_array(self.classifier.predict(x, batch_size=self.batch_size))
# Compute perturbation with implicit batching
nb_batches = int(np.ceil(x_adv.shape[0] / float(self.batch_size)))
for batch_id in range(nb_batches):
logger.debug('Processing batch %i out of %i', batch_id, nb_batches)
batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
x_batch = x_adv[batch_index_1:batch_index_2]
y_batch = y[batch_index_1:batch_index_2]
# Determine values for later clipping
clip_min = np.clip(x_batch - self.eps, clip_min_per_pixel, clip_max_per_pixel)
clip_max = np.clip(x_batch + self.eps, clip_min_per_pixel, clip_max_per_pixel)
# The optimization is performed in tanh space to keep the
# adversarial images bounded from clip_min and clip_max.
x_batch_tanh = original_to_tanh(x_batch, clip_min, clip_max, self._tanh_smoother)
# Initialize perturbation in tanh space:
x_adv_batch = x_batch.copy()
x_adv_batch_tanh = x_batch_tanh.copy()
# Initialize optimization:
z_logits, loss = self._loss(x_adv_batch, y_batch)
attack_success = (loss <= 0)
learning_rate = self.learning_rate * np.ones(x_batch.shape[0])
for i_iter in range(self.max_iter):
logger.debug('Iteration step %i out of %i', i_iter, self.max_iter)
logger.debug('Average Loss: %f', np.mean(loss))
logger.debug('Successful attack samples: %i out of %i', int(np.sum(attack_success)), x_batch.shape[0])
# only continue optimization for those samples where attack hasn't succeeded yet:
active = ~attack_success
if np.sum(active) == 0:
break
# compute gradient:
logger.debug('Compute loss gradient')
perturbation_tanh = -self._loss_gradient(z_logits[active], y_batch[active], x_adv_batch[active],
x_adv_batch_tanh[active], clip_min[active], clip_max[active])
# perform line search to optimize perturbation
# first, halve the learning rate until perturbation actually decreases the loss:
prev_loss = loss.copy()
best_loss = loss.copy()
best_lr = np.zeros(x_batch.shape[0])
halving = np.zeros(x_batch.shape[0])
for i_halve in range(self.max_halving):
logger.debug('Perform halving iteration %i out of %i', i_halve, self.max_halving)
do_halving = (loss[active] >= prev_loss[active])
logger.debug('Halving to be performed on %i samples', int(np.sum(do_halving)))
if np.sum(do_halving) == 0:
break
active_and_do_halving = active.copy()
active_and_do_halving[active] = do_halving
lr_mult = learning_rate[active_and_do_halving]
for _ in range(len(x.shape) - 1):
lr_mult = lr_mult[:, np.newaxis]
new_x_adv_batch_tanh = x_adv_batch_tanh[active_and_do_halving] + lr_mult * perturbation_tanh[
do_halving]
new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh,
clip_min[active_and_do_halving],
clip_max[active_and_do_halving])
_, loss[active_and_do_halving] = self._loss(new_x_adv_batch, y_batch[active_and_do_halving])
logger.debug('New Average Loss: %f', np.mean(loss))
logger.debug('Loss: %s', str(loss))
logger.debug('Prev_loss: %s', str(prev_loss))
logger.debug('Best_loss: %s', str(best_loss))
best_lr[loss < best_loss] = learning_rate[loss < best_loss]
best_loss[loss < best_loss] = loss[loss < best_loss]
learning_rate[active_and_do_halving] /= 2
halving[active_and_do_halving] += 1
learning_rate[active] *= 2
# if no halving was actually required, double the learning rate as long as this
# decreases the loss:
for i_double in range(self.max_doubling):
logger.debug('Perform doubling iteration %i out of %i', i_double, self.max_doubling)
do_doubling = (halving[active] == 1) & (loss[active] <= best_loss[active])
logger.debug('Doubling to be performed on %i samples', int(np.sum(do_doubling)))
if np.sum(do_doubling) == 0:
break
active_and_do_doubling = active.copy()
active_and_do_doubling[active] = do_doubling
learning_rate[active_and_do_doubling] *= 2
lr_mult = learning_rate[active_and_do_doubling]
for _ in range(len(x.shape) - 1):
lr_mult = lr_mult[:, np.newaxis]
new_x_adv_batch_tanh = x_adv_batch_tanh[active_and_do_doubling] + lr_mult * perturbation_tanh[
do_doubling]
new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh,
clip_min[active_and_do_doubling],
clip_max[active_and_do_doubling])
_, loss[active_and_do_doubling] = self._loss(new_x_adv_batch,
y_batch[active_and_do_doubling])
logger.debug('New Average Loss: %f', np.mean(loss))
best_lr[loss < best_loss] = learning_rate[loss < best_loss]
best_loss[loss < best_loss] = loss[loss < best_loss]
learning_rate[halving == 1] /= 2
update_adv = (best_lr[active] > 0)
logger.debug('Number of adversarial samples to be finally updated: %i', int(np.sum(update_adv)))
if np.sum(update_adv) > 0:
active_and_update_adv = active.copy()
active_and_update_adv[active] = update_adv
best_lr_mult = best_lr[active_and_update_adv]
for _ in range(len(x.shape) - 1):
best_lr_mult = best_lr_mult[:, np.newaxis]
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[active_and_update_adv] + best_lr_mult * \
perturbation_tanh[update_adv]
x_adv_batch[active_and_update_adv] = tanh_to_original(x_adv_batch_tanh[active_and_update_adv],
clip_min[active_and_update_adv],
clip_max[active_and_update_adv])
z_logits[active_and_update_adv], loss[active_and_update_adv] = self._loss(
x_adv_batch[active_and_update_adv], y_batch[active_and_update_adv])
attack_success = (loss <= 0)
# Update depending on attack success:
x_adv_batch[~attack_success] = x_batch[~attack_success]
x_adv[batch_index_1:batch_index_2] = x_adv_batch
logger.info('Success rate of C&W L_inf attack: %.2f%%',
100 * compute_success(self.classifier, x, y, x_adv, self.targeted, batch_size=self.batch_size))
return x_adv
def set_params(self, **kwargs):
"""Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
:param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
from the original input, but classified with higher confidence as the target class.
:type confidence: `float`
:param targeted: Should the attack target one specific class
:type targeted: `bool`
:param learning_rate: The learning rate for the attack algorithm. Smaller values produce better results but are
slower to converge.
:type learning_rate: `float`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param max_halving: Maximum number of halving steps in the line search optimization.
:type max_halving: `int`
:param max_doubling: Maximum number of doubling steps in the line search optimization.
:type max_doubling: `int`
:param eps: An upper bound for the L_0 norm of the adversarial perturbation.
:type eps: `float`
:param batch_size: Internal size of batches on which adversarial samples are generated.
:type batch_size: `int`
"""
# Save attack-specific parameters
super(CarliniLInfMethod, self).set_params(**kwargs)
if self.eps <= 0:
raise ValueError("The eps parameter must be strictly positive.")
if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
raise ValueError("The number of iterations must be a non-negative integer.")
if not isinstance(self.max_halving, (int, np.int)) or self.max_halving < 1:
raise ValueError("The number of halving steps must be an integer greater than zero.")
if not isinstance(self.max_doubling, (int, np.int)) or self.max_doubling < 1:
raise ValueError("The number of doubling steps must be an integer greater than zero.")
if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 1:
raise ValueError("The batch size must be an integer greater than zero.")
return True