-
Notifications
You must be signed in to change notification settings - Fork 2
/
create_transferable_stAdv_samples.py
182 lines (138 loc) · 6.12 KB
/
create_transferable_stAdv_samples.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
# The code for the stAdv attack has been borrowed from: https://github.com/rakutentech/stAdv
import csv
from util import util
import metrics_and_dataloaders as mdl
import util.path as paths
import numpy as np
import torch
import torch.nn.functional as F
from tqdm import tqdm
from scipy import optimize
import os
torch.manual_seed(0)
torch.autograd.set_detect_anomaly(True)
def flow_st(images,flows):
images_shape = images.size()
flows_shape = flows.size()
batch_size = images_shape[0]
H = images_shape[2]
W = images_shape[3]
basegrid = torch.stack(torch.meshgrid(torch.arange(0,H), torch.arange(0,W))) #(2,H,W)
sampling_grid = basegrid.unsqueeze(0).type(torch.float32).cuda() + flows.cuda()
sampling_grid_x = torch.clamp(sampling_grid[:,1],0.0,W-1.0).type(torch.float32)
sampling_grid_y = torch.clamp(sampling_grid[:,0],0.0,H-1.0).type(torch.float32)
x0 = torch.floor(sampling_grid_x).type(torch.int64)
x1 = x0 + 1
y0 = torch.floor(sampling_grid_y).type(torch.int64)
y1 = y0 + 1
x0 = torch.clamp(x0, 0, W - 2)
x1 = torch.clamp(x1, 0, W - 1)
y0 = torch.clamp(y0, 0, H - 2)
y1 = torch.clamp(y1, 0, H - 1)
Ia = images[:,:,y0[0,:,:], x0[0,:,:]]
Ib = images[:,:,y1[0,:,:], x0[0,:,:]]
Ic = images[:,:,y0[0,:,:], x1[0,:,:]]
Id = images[:,:,y1[0,:,:], x1[0,:,:]]
x0 = x0.type(torch.float32)
x1 = x1.type(torch.float32)
y0 = y0.type(torch.float32)
y1 = y1.type(torch.float32)
wa = (x1 - sampling_grid_x) * (y1 - sampling_grid_y)
wb = (x1 - sampling_grid_x) * (sampling_grid_y - y0)
wc = (sampling_grid_x - x0) * (y1 - sampling_grid_y)
wd = (sampling_grid_x - x0) * (sampling_grid_y - y0)
perturbed_image = wa.unsqueeze(0)*Ia+wb.unsqueeze(0)*Ib+wc.unsqueeze(0)*Ic+wd.unsqueeze(0)*Id
return perturbed_image.type(torch.float32).cuda()
def flow_loss(flows,padding_mode='constant', epsilon=1e-8):
paddings = (1,1,1,1)
padded_flows = F.pad(flows,paddings,mode=padding_mode,value=0)
shifted_flows = [
padded_flows[:, :, 2:, 2:], # bottom right (+1,+1)
padded_flows[:, :, 2:, :-2], # bottom left (+1,-1)
padded_flows[:, :, :-2, 2:], # top right (-1,+1)
padded_flows[:, :, :-2, :-2] # top left (-1,-1)
]
#||\Delta u^{(p)} - \Delta u^{(q)}||_2^2 + # ||\Delta v^{(p)} - \Delta v^{(q)}||_2^2
loss=0
for shifted_flow in shifted_flows:
loss += torch.sum(torch.square(flows[:, 1] - shifted_flow[:, 1]) + torch.square(flows[:, 0] - shifted_flow[:, 0]) + epsilon).cuda()
return loss.type(torch.float32)
def metric_d(x0, x1, xref):
d0 = metric.forward(xref,x0)
d1 = metric.forward(xref,x1)
return d0, d1
def rank_loss(s_adv, s_other):
return (s_other/(s_adv+s_other)).float().clone()
def func(flows, x_prey, x_other, x_ref, alpha=50, beta=0.05):
flows = torch.from_numpy(flows).view((1,2,)+x_prey.size()[2:]).cuda()
flows.requires_grad=True
pert_out = flow_st(x_prey,flows)
s_adv, s_other = metric_d(pert_out, x_other, x_ref)
L_adv = rank_loss(s_adv, s_other)
L_flow = flow_loss(flows)
L_final = alpha*L_adv+beta*L_flow
metric.net.zero_grad()
L_final.backward()
gradient = flows.grad.data.view(-1).detach().cpu().numpy()
if s_adv>s_other:
return 0, gradient
return L_final.item(), gradient
def attack(x_prey, x_other, x_ref, max_iter=50):
init_flows = np.zeros((1,2,)+x_prey.size()[2:]).reshape(-1)
results = optimize.fmin_l_bfgs_b(func, init_flows, args=(x_prey, x_other, x_ref), maxiter=max_iter, disp=False)
return results
##### ##### ##### ##### #####
# Load metric
metric, transform, model_frwd_func = mdl.get_perceptual_similarity_metric("lpipsAlex", 64) # warning: hardcoded parameters for metric and bapps image size
# Setting GPU use to True here
use_gpu = True
# Load samples list
bapps_samples = list(np.load(paths.BAPPS_clearWinner_samples_file))
version = "YourDatasetVersionNameGoesHere"
results_path = "./datasets/transferableAdvSamples_"+version
if not os.path.isdir(results_path+"/stAdv/"):
os.makedirs(results_path+"/stAdv/")
output_file = results_path+"/stAdv_flippedSamples.csv"
with open(output_file, mode='w') as f:
w = csv.writer(f, delimiter=',')
w.writerow(["path", "rank", "new_rank", "rmse", "d0", "d1", "dAdv"])
for impath in tqdm(bapps_samples):
x_0, x_1, x_ref = mdl.load_data(impath, transform)
if use_gpu:
x_0, x_1, x_ref = x_0.cuda(), x_1.cuda(), x_ref.cuda()
d0, d1 = metric_d(x_0, x_1, x_ref)
rank = int((d0>d1).item())
if rank == 0:
x_prey = x_0
x_other = x_1
else:
x_prey = x_1
x_other = x_0
results = attack(x_prey, x_other, x_ref)
flows = torch.from_numpy(results[0]).view((1,2,)+x_prey.size()[2:])
x_adv = flow_st(x_prey, flows)
s_adv = metric.forward(x_adv, x_ref)
s_prey = metric.forward(x_prey, x_ref)
s_other = metric.forward(x_other, x_ref)
xprey_im = util.tensor2im(x_prey).astype(np.float32)
xadv_im = util.tensor2im(x_adv).astype(np.float32)
rmse = np.sqrt(np.square(xadv_im-xprey_im).mean())
impathname = impath.split("/")
impathname = results_path+"/stAdv/"+impathname[-3]+"_"+impathname[-1].split(".")[0]
new_rank = rank
if s_adv > s_other: # Saving image only if the rank flipped
#rank flipped
new_rank = int(not(rank))
util.save_image(util.tensor2im(x_adv.detach()),impathname+".png")
row = [impath, rank, new_rank, np.round(rmse,3)]
if rank == 0:
row += [np.round(s_prey.item(),3), np.round(s_other.item(),3), np.round(s_adv.item(),3)]
else:
row += [np.round(s_other.item(),3), np.round(s_prey.item(),3), np.round(s_adv.item(),3)]
w.writerow(row)
# print(s_adv.item(),s_prey.item(),s_other.item())
# print(s_adv>s_other)
# plt.imshow(util.tensor2im(x_prey))
# plt.show()
# plt.imshow(util.tensor2im(pert_out))
# plt.show()