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verification_attack.py
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verification_attack.py
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import argparse
import os
import os.path as osp
import sys
import torch
import torch.nn as nn
from torch import optim
import verification_model
from celeba_data import create_dic
from celeba_solver import Celeba_Solver
from utils import TVLoss, rec_transform, save_image
sys.path.append('../')
from attacks import semantic_attack
def denorm1(x):
"""Convert the range from [-1, 1] to [0, 1]."""
out = (x + 1) / 2
return out.clamp_(0, 1)
class End_Model(nn.Module):
def __init__(self, net):
super(End_Model, self).__init__()
self.net = net
def forward(self, x):
face_feature = self.net(rec_transform(x))
normed_face_feature = face_feature / torch.norm(face_feature, dim=1)
return normed_face_feature
def main(config):
success_records = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
fail_records = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
attack_records = []
save_path = config.save_path
threshold = config.threshold
if threshold > 0 and config.untargeted == True:
threshold = -threshold
if config.test_threshold == 0:
test_threshold = threshold
else:
test_threshold = config.test_threshold
solver = Celeba_Solver(config)
model = verification_model.resnet101(feature_dim=config.feature_dim)
model = verification_model.IdentityMapping(model)
model.cuda()
model = torch.nn.DataParallel(model).cuda()
verification_model.load_ckpt(config.load_path, model, strict=False)
end_model = End_Model(model)
criterionL2 = torch.nn.MSELoss()
adversary = semantic_attack.FP_CW_TV(config.lr, config.max_iteration,
config.tv_lambda,
threshold / 256)
# Read images
test_idlist = []
test_namelist = []
if config.data_mode == 'demo':
sub_folder = ''
data_list = [0, 2, 10, 24, 28, 69]
if config.untargeted == True:
original_list = [0, 1, 2, 3, 4, 5]
else:
original_list = [1, 2, 3, 4, 5]
elif config.data_mode == 'all':
sub_folder = 'ori/'
data_list = range(5000)
original_list = range(1280)
for temp_i in data_list:
temp_path1 = config.celeba_image_dir + str(temp_i + 1) + '/' + sub_folder
test_idlist.append(str(temp_i + 1))
test_namelist.append(sorted(os.listdir(temp_path1))[0])
dic_label, dic_image = create_dic(config.celeba_image_dir,
config.attr_path, config.selected_attrs,
config.celeba_crop_size,
config.image_size, test_idlist,
test_namelist, sub_folder)
dic_attribute = dict(zip(['Blond_Hair', 'Wavy_Hair', 'Young', 'Eyeglasses', 'Heavy_Makeup', 'Rosy_Cheeks',
'Chubby', 'Mouth_Slightly_Open', 'Bushy_Eyebrows', 'Wearing_Lipstick', 'Smiling',
'Arched_Eyebrows', 'Bangs', 'Wearing_Earrings', 'Bags_Under_Eyes', 'Receding_Hairline', 'Pale_Skin'],
range(17)))
for index in original_list:
if config.untargeted == True:
i_target = index
else:
if config.data_mode == 'demo':
i_target = 0
elif config.data_mode == 'all':
i_target = index + 2000
t_x_real = dic_image[test_namelist[i_target]]
t_x_real = t_x_real.unsqueeze(0)
t_img_ori = rec_transform(denorm1(t_x_real)).cuda()
t_ori_face_feature = model(t_img_ori)
t_face_feature_const = torch.zeros_like(t_ori_face_feature)
t_face_feature_const.data = t_ori_face_feature.clone()
t_face_feature_const = t_face_feature_const / torch.norm(
t_face_feature_const, dim=1)
save_image(
save_path + str(index) + '_' + str(i_target) + '_' +
'target_img.png', denorm1(t_x_real))
c_org = dic_label[test_namelist[index]]
c_org = c_org.unsqueeze(0)
x_real = dic_image[test_namelist[index]]
x_real = x_real.unsqueeze(0)
c_org = c_org.cuda()
x_real = x_real.cuda()
x_real_constant = denorm1(x_real).clone().cuda()
save_image(
save_path + str(index) + '_' + str(i_target) + '_' +
'original_img.png', x_real_constant)
delta = torch.zeros_like(c_org)
delta = delta.cuda()
x_real.requires_grad = True
optimizer = optim.Adam([x_real], lr=0.01)
# Opitimize X to get X'. G(X',c) looks more similar than G(X,c).
for z in range(300):
denormed_adv = solver.enc(delta, x_real, c_org)
edit_final = solver.dec(denormed_adv)
img_loss = criterionL2(edit_final, x_real_constant)
face_loss = criterionL2(denorm1(x_real), x_real_constant)
loss = img_loss + face_loss * 1.0
optimizer.zero_grad()
loss.backward()
optimizer.step()
x_real.data = torch.clamp(x_real.data, -1, 1)
new_x_real = x_real.clone().cuda()
with torch.no_grad():
generated_ori = solver.enc(delta, new_x_real, c_org)
if config.adv_attribute == 'all':
attribute_list = range(17)
else:
attribute_list = [dic_attribute[config.adv_attribute]]
for j in attribute_list:
delta = torch.zeros_like(c_org)
delta[:, j] = delta[:, j] + 1
delta = delta.cuda()
with torch.no_grad():
denormed_adv = solver.enc(delta, new_x_real, c_org)
edit_final, adv_loss, tv_loss = adversary(
G_dec=solver.dec,
emb1=generated_ori,
emb2=denormed_adv,
model=end_model,
loss_func=criterionL2,
target_label=t_face_feature_const,
targeted=(not config.untargeted))
adv_dist = adv_loss.item() * 256
tv_dist = tv_loss.item()
print('source id:', index, ', target id:', i_target,
', attribute index:', j, ', feature distance:', adv_dist,
', attack result:', adv_dist < test_threshold)
save_image(
save_path + str(index) + '_' + str(i_target) + '_' +
str(adv_dist < test_threshold) + '_' + 'adv_G(X,c' + str(j) +
').png', edit_final)
if adv_dist < test_threshold:
attack_records.append([
index, i_target, j,
round(adv_dist, 4),
round(tv_dist, 5), True
])
success_records[j] += 1
else:
attack_records.append([
index, i_target, j,
round(adv_dist, 4),
round(tv_dist, 5), False
])
fail_records[j] += 1
rate_list = []
for attr in range(17):
if (success_records[attr] + fail_records[attr]) > 0:
rate_list.append(success_records[attr] /
(success_records[attr] + fail_records[attr]))
print('success rate for each attribute:', rate_list)
if not osp.exists('./' + save_path):
os.makedirs('./' + save_path)
f = open(save_path + 'record.txt', 'w')
f.write(
'source id, target id, attribute index, feature distance, tv loss, attack result'
+ '\n')
for length in range(len(attack_records)):
f.write(str(attack_records[length])[1:-1] + '\n')
f.write('success rate for each attribute: ' + str(rate_list)[1:-1] + '\n')
f.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Model configuration.
parser.add_argument('--c_dim',
type=int,
default=17,
help='dimension of domain labels (1st dataset)')
parser.add_argument('--celeba_crop_size',
type=int,
default=112,
help='crop size for the CelebA dataset')
parser.add_argument('--image_size',
type=int,
default=112,
help='image resolution')
parser.add_argument('--g_conv_dim',
type=int,
default=128,
help='number of conv filters in the first layer of G')
parser.add_argument('--d_conv_dim',
type=int,
default=128,
help='number of conv filters in the first layer of D')
parser.add_argument('--g_repeat_num',
type=int,
default=6,
help='number of residual blocks in G')
parser.add_argument('--d_repeat_num',
type=int,
default=6,
help='number of strided conv layers in D')
parser.add_argument('--dataset',
type=str,
default='CelebA',
choices=['CelebA'],
help='which dataset to use, only support CelebA currently')
parser.add_argument('--c2_dim',
type=int,
default=8,
help='dimension of domain labels (2nd dataset)')
parser.add_argument('--selected_attrs',
'--list',
nargs='+',
help='selected attributes for the CelebA dataset',
default=[
'Blond_Hair', 'Wavy_Hair', 'Young', 'Eyeglasses',
'Heavy_Makeup', 'Rosy_Cheeks', 'Chubby',
'Mouth_Slightly_Open', 'Bushy_Eyebrows',
'Wearing_Lipstick', 'Smiling', 'Arched_Eyebrows',
'Bangs', 'Wearing_Earrings', 'Bags_Under_Eyes',
'Receding_Hairline', 'Pale_Skin'
])
# Test configuration.
parser.add_argument('--test_iters',
type=int,
default=200000,
help='test model from this step')
# Miscellaneous.
parser.add_argument('--num_workers', type=int, default=1, help='number of workers')
# Directories.
parser.add_argument('--celeba_image_dir',
type=str,
default='./aligned_id_divied_imgs/',
help='path of face images')
parser.add_argument('--attr_path',
type=str,
default='./list_attr_celeba.txt',
help='path of face attributes')
parser.add_argument('--model_save_dir',
type=str,
default='./pretrain_models/',
help='path of pretrained stargan model')
# Face Recognition
parser.add_argument('--feature_dim', default=256, type=int,
help='feature dimensions for face verification')
parser.add_argument('--load_path',
type=str,
default='./pretrain_models/res101_softmax.pth.tar',
help='path of pretrained face verification model')
# Please don't change above setting.
# You can change below setting.
parser.add_argument('--max_iteration', type=int, default=200,
help='maximum iterations')
parser.add_argument('--lr', type=float, default=0.05,
help='learning rate')
parser.add_argument('--tv_lambda', type=float, default=0.01, help='lambda for tv loss')
parser.add_argument('--threshold', type=float, default=1.244,
help='threshold for face verification, 1.244 for fpr=10e-3,0.597 for fpr=10e-4')
parser.add_argument('--save_path', type=str, default='results/', help='path to save the results')
parser.add_argument('--interp_layer', type=str, default='0', choices=['0', '1', '2', '01', '02'], help='which layer to interpolate')
parser.add_argument('--test_threshold', type=float, default=0)
parser.add_argument('--untargeted', action='store_true', help='targeted or untargeted')
parser.add_argument('--data_mode', type=str, default='demo', choices=['demo', 'all'], help='demo mode for simple demo, all mode for paper reproduction')
parser.add_argument('--adv_attribute', type=str, default='all', choices=[
'Blond_Hair', 'Wavy_Hair', 'Young', 'Eyeglasses',
'Heavy_Makeup', 'Rosy_Cheeks', 'Chubby',
'Mouth_Slightly_Open', 'Bushy_Eyebrows',
'Wearing_Lipstick', 'Smiling', 'Arched_Eyebrows',
'Bangs', 'Wearing_Earrings', 'Bags_Under_Eyes',
'Receding_Hairline', 'Pale_Skin', 'all'
], help='which attribute to use')
config = parser.parse_args()
print(config)
main(config)