-
Notifications
You must be signed in to change notification settings - Fork 13
/
zoo_l2_attack_black.py
352 lines (303 loc) · 12.1 KB
/
zoo_l2_attack_black.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
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from torchvision import transforms,datasets
from numba import jit
import math
import time
import scipy.misc
import os
import sys
from PIL import Image
from setup_mnist_model import MNIST
from setup_cifar10_model import CIFAR10
"""##L2 Black Box Attack"""
@jit(nopython=True)
def coordinate_ADAM(losses, indice, grad, hess, batch_size, mt_arr, vt_arr, real_modifier, adam_epoch, up, down, step_size,beta1, beta2, proj):
for i in range(batch_size):
grad[i] = (losses[i*2+1] - losses[i*2+2]) / 0.0002
# ADAM update
mt = mt_arr[indice]
mt = beta1 * mt + (1 - beta1) * grad
mt_arr[indice] = mt
vt = vt_arr[indice]
vt = beta2 * vt + (1 - beta2) * (grad * grad)
vt_arr[indice] = vt
epoch = adam_epoch[indice]
corr = (np.sqrt(1 - np.power(beta2,epoch))) / (1 - np.power(beta1, epoch))
m = real_modifier.reshape(-1)
old_val = m[indice]
old_val -= step_size * corr * mt / (np.sqrt(vt) + 1e-8)
# set it back to [-0.5, +0.5] region
if proj:
old_val = np.maximum(np.minimum(old_val, up[indice]), down[indice])
m[indice] = old_val
adam_epoch[indice] = epoch + 1
@jit(nopython=True)
def coordinate_Newton(losses, indice, grad, hess, batch_size, mt_arr, vt_arr, real_modifier, adam_epoch, up, down, step_size, beta1, beta2, proj):
cur_loss = losses[0]
for i in range(batch_size):
grad[i] = (losses[i*2+1] - losses[i*2+2]) / 0.0002
hess[i] = (losses[i*2+1] - 2 * cur_loss + losses[i*2+2]) / (0.0001 * 0.0001)
hess[hess < 0] = 1.0
hess[hess < 0.1] = 0.1
m = real_modifier.reshape(-1)
old_val = m[indice]
old_val -= step_size * grad / hess
# set it back to [-0.5, +0.5] region
if proj:
old_val = np.maximum(np.minimum(old_val, up[indice]), down[indice])
m[indice] = old_val
def loss_run(input,target,model,modifier,use_tanh,use_log,targeted,confidence,const):
if use_tanh:
pert_out = torch.tanh(input +modifier)/2
else:
pert_out = input + modifier
output = model(pert_out)
if use_log:
output = F.softmax(output,-1)
if use_tanh:
loss1 = torch.sum(torch.square(pert_out-torch.tanh(input)/2),dim=(1,2,3))
else:
loss1 = torch.sum(torch.square(pert_out-input),dim=(1,2,3))
real = torch.sum(target*output,-1)
other = torch.max((1-target)*output-(target*10000),-1)[0]
if use_log:
real=torch.log(real+1e-30)
other=torch.log(other+1e-30)
confidence = torch.tensor(confidence).type(torch.float64).cuda()
if targeted:
loss2 = torch.max(other-real,confidence)
else:
loss2 = torch.max(real-other,confidence)
loss2 = const*loss2
l2 = loss1
loss = loss1 + loss2
return loss.detach().cpu().numpy(), l2.detach().cpu().numpy(), loss2.detach().cpu().numpy(), output.detach().cpu().numpy(), pert_out.detach().cpu().numpy()
def l2_attack(input, target, model, targeted, use_log, use_tanh, solver, reset_adam_after_found=True,abort_early=True,
batch_size=128,max_iter=1000,const=0.01,confidence=0.0,early_stop_iters=100, binary_search_steps=9,
step_size=0.01,adam_beta1=0.9,adam_beta2=0.999):
early_stop_iters = early_stop_iters if early_stop_iters != 0 else max_iter // 10
input = torch.from_numpy(input).cuda()
target = torch.from_numpy(target).cuda()
var_len = input.view(-1).size()[0]
modifier_up = np.zeros(var_len, dtype=np.float32)
modifier_down = np.zeros(var_len, dtype=np.float32)
real_modifier = torch.zeros(input.size(),dtype=torch.float32).cuda()
mt = np.zeros(var_len, dtype=np.float32)
vt = np.zeros(var_len, dtype=np.float32)
adam_epoch = np.ones(var_len, dtype=np.int32)
grad=np.zeros(batch_size,dtype=np.float32)
hess=np.zeros(batch_size,dtype=np.float32)
upper_bound=1e10
lower_bound=0.0
out_best_attack=input.clone().detach().cpu().numpy()
out_best_const=const
out_bestl2=1e10
out_bestscore=-1
if use_tanh:
input = torch.atanh(input*1.99999)
if not use_tanh:
modifier_up = 0.5-input.clone().detach().view(-1).cpu().numpy()
modifier_down = -0.5-input.clone().detach().view(-1).cpu().numpy()
def compare(x,y):
if not isinstance(x, (float, int, np.int64)):
if targeted:
x[y] -= confidence
else:
x[y] += confidence
x = np.argmax(x)
if targeted:
return x == y
else:
return x != y
for step in range(binary_search_steps):
bestl2 = 1e10
prev=1e6
bestscore=-1
last_loss2=1.0
# reset ADAM status
mt.fill(0)
vt.fill(0)
adam_epoch.fill(1)
stage=0
for iter in range(max_iter):
if (iter+1)%100 == 0:
loss, l2, loss2, _ , __ = loss_run(input,target,model,real_modifier,use_tanh,use_log,targeted,confidence,const)
print("[STATS][L2] iter = {}, loss = {:.5f}, loss1 = {:.5f}, loss2 = {:.5f}".format(iter+1, loss[0], l2[0], loss2[0]))
sys.stdout.flush()
var_list = np.array(range(0, var_len), dtype = np.int32)
indice = var_list[np.random.choice(var_list.size, batch_size, replace=False)]
var = np.repeat(real_modifier.detach().cpu().numpy(), batch_size * 2 + 1, axis=0)
for i in range(batch_size):
var[i*2+1].reshape(-1)[indice[i]]+=0.0001
var[i*2+2].reshape(-1)[indice[i]]-=0.0001
var = torch.from_numpy(var)
var = var.view((-1,)+input.size()[1:]).cuda()
losses, l2s, losses2, scores, pert_images = loss_run(input,target,model,var,use_tanh,use_log,targeted,confidence,const)
real_modifier_numpy = real_modifier.clone().detach().cpu().numpy()
if solver=="adam":
coordinate_ADAM(losses,indice,grad,hess,batch_size,mt,vt,real_modifier_numpy,adam_epoch,modifier_up,modifier_down,step_size,adam_beta1,adam_beta2,proj=not use_tanh)
if solver=="newton":
coordinate_Newton(losses,indice,grad,hess,batch_size,mt,vt,real_modifier_numpy,adam_epoch,modifier_up,modifier_down,step_size,adam_beta1,adam_beta2,proj=not use_tanh)
real_modifier=torch.from_numpy(real_modifier_numpy).cuda()
if losses2[0]==0.0 and last_loss2!=0.0 and stage==0:
if reset_adam_after_found:
mt.fill(0)
vt.fill(0)
adam_epoch.fill(1)
stage=1
last_loss2=losses2[0]
if abort_early and (iter+1) % early_stop_iters == 0:
if losses[0] > prev*.9999:
print("Early stopping because there is no improvement")
break
prev = losses[0]
if l2s[0] < bestl2 and compare(scores[0], np.argmax(target.cpu().numpy(),-1)):
bestl2 = l2s[0]
bestscore = np.argmax(scores[0])
if l2s[0] < out_bestl2 and compare(scores[0],np.argmax(target.cpu().numpy(),-1)):
if out_bestl2 == 1e10:
print("[STATS][L3](First valid attack found!) iter = {}, loss = {:.5f}, loss1 = {:.5f}, loss2 = {:.5f}".format(iter+1, losses[0], l2s[0], losses2[0]))
sys.stdout.flush()
out_bestl2 = l2s[0]
out_bestscore = np.argmax(scores[0])
out_best_attack = pert_images[0]
out_best_const = const
if compare(bestscore, np.argmax(target.cpu().numpy(),-1)) and bestscore != -1:
print('old constant: ', const)
upper_bound = min(upper_bound,const)
if upper_bound < 1e9:
const = (lower_bound + upper_bound)/2
print('new constant: ', const)
else:
print('old constant: ', const)
lower_bound = max(lower_bound,const)
if upper_bound < 1e9:
const = (lower_bound + upper_bound)/2
else:
const *= 10
print('new constant: ', const)
return out_best_attack, out_bestscore
def generate_data(test_loader,targeted,samples,start):
inputs=[]
targets=[]
num_label=10
cnt=0
for i, data in enumerate(test_loader):
if cnt<samples:
if i>start:
data, label = data[0],data[1]
if targeted:
seq = range(num_label)
for j in seq:
if j==label.item():
continue
inputs.append(data[0].numpy())
targets.append(np.eye(num_label)[j])
else:
inputs.append(data[0].numpy())
targets.append(np.eye(num_label)[label.item()])
cnt+=1
else:
continue
else:
break
inputs=np.array(inputs)
targets=np.array(targets)
return inputs,targets
def attack(inputs, targets, model, targeted, use_log, use_tanh, solver, device):
r = []
print('go up to',len(inputs))
# run 1 image at a time, minibatches used for gradient evaluation
for i in range(len(inputs)):
print('tick',i+1)
attack,score=l2_attack(np.expand_dims(inputs[i],0), np.expand_dims(targets[i],0), model, targeted, use_log, use_tanh, solver, device)
r.append(attack)
return np.array(r)
if __name__=='__main__':
np.random.seed(42)
torch.manual_seed(42)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
# test_set = datasets.MNIST(root = './data', train=False, transform = transform, download=True)
test_set = datasets.CIFAR10(root = './data', train=False, transform = transform, download=True)
test_loader = torch.utils.data.DataLoader(test_set,batch_size=1,shuffle=True)
use_cuda=True
device = torch.device("cuda" if (use_cuda and torch.cuda.is_available()) else "cpu")
# model = MNIST().to(device)
model = CIFAR10().to(device)
# model.load_state_dict(torch.load('./models/mnist_model.pt'))
model.load_state_dict(torch.load('./models/cifar10_model.pt'))
model.eval()
use_log=True
use_tanh=True
targeted=True
solver="newton"
#start is a offset to start taking sample from test set
#samples is the how many samples to take in total : for targeted, 1 means all 9 class target -> 9 total samples whereas for untargeted the original data
#sample is taken i.e. 1 sample only
inputs, targets = generate_data(test_loader,targeted,samples=10,start=6)
timestart = time.time()
adv = attack(inputs, targets, model, targeted, use_log, use_tanh, solver, device)
timeend = time.time()
print("Took",(timeend-timestart)/60.0,"mins to run",len(inputs),"samples.")
if use_log:
valid_class = np.argmax(F.softmax(model(torch.from_numpy(inputs).cuda()),-1).detach().cpu().numpy(),-1)
adv_class = np.argmax(F.softmax(model(torch.from_numpy(adv).cuda()),-1).detach().cpu().numpy(),-1)
else:
valid_class = np.argmax(model(torch.from_numpy(inputs).cuda()).detach().cpu().numpy(),-1)
adv_class = np.argmax(model(torch.from_numpy(adv).cuda()).detach().cpu().numpy(),-1)
acc = ((valid_class==adv_class).sum())/len(inputs)
print("Valid Classification: ", valid_class)
print("Adversarial Classification: ", adv_class)
print("Success Rate: ", (1.0-acc)*100.0)
print("Total distortion: ", np.sum((adv-inputs)**2)**.5)
# for saving the mnist samples
# for i in range(len(inputs)):
# save(inputs[i], "original_"+str(i)+".png")
# save(adv[i], "adversarial_"+str(i)+".png")
# save(adv[i] - inputs[i], "diff_"+str(i)+".png")
#visualization of created mnist adv examples
# cnt=0
# plt.figure(figsize=(10,10))
# for i in range(len(adv)):
# cnt+=1
# plt.subplot(10,10,cnt)
# plt.xticks([], [])
# plt.yticks([], [])
# plt.title("{} -> {}".format(valid_class[i],adv_class[i]))
# plt.imshow(adv[i].reshape(28,28), cmap="gray")
# plt.tight_layout()
# if targeted:
# if solver=="newton":
# plt.savefig('newton_targeted_mnist.png')
# else:
# plt.savefig('adam_targeted_mnist.png')
# else:
# if solver=="newton":
# plt.savefig('newton_untargeted_mnist.png')
# else:
# plt.savefig('adam_untargeted_mnist.png')
#visualization of created cifar10 adv examples
classes = ('plane', 'car', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck')
cnt=0
plt.figure(figsize=(10,10))
for i in range(len(adv)):
cnt+=1
plt.subplot(10,10,cnt)
plt.xticks([], [])
plt.yticks([], [])
plt.title("{}->{}".format(classes[valid_class[i]],classes[adv_class[i]]))
plt.imshow(((adv[i]+0.5)).transpose(1,2,0))
plt.tight_layout()
if targeted:
if solver=="newton":
plt.savefig('newton_targeted_cifar10.png')
else:
plt.savefig('adam_targeted_cifar10.png')
else:
if solver=="newton":
plt.savefig('newton_untargeted_cifar10.png')
else:
plt.savefig('adam_untargeted_cifar10.png')