def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# model920 = model_920().to(device)
model920_facenet_criterion = nn.MSELoss()
# model921 = model_921()
# model921_facenet_criterion = nn.MSELoss()
InceptionResnet_model_1 = InceptionResnetV1(
pretrained='vggface2').eval().to(device)
print('load InceptionResnet-vggface2.pt successfully')
InceptionResnet_model_2 = InceptionResnetV1(
pretrained='casia-webface').eval().to(device)
print('load InceptionResnet-casia-webface.pt successfully')
IR_50_model_1 = IR_50([112, 112])
IR_50_model_1.load_state_dict(
torch.load(
'/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/backbone_ir50_asia.pth'
))
IR_50_model_1.eval().to(device)
print('load IR_50 successfully')
IR_152_model_1 = IR_152([112, 112])
IR_152_model_1.load_state_dict(
torch.load(
'/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth'
))
IR_152_model_1.eval().to(device)
print('load IR_152 successfully')
# IR_152_model_2 = IR_152([112, 112])
# IR_152_model_2.load_state_dict(
# torch.load(
# '/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/Head_ArcFace_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth'))
# IR_152_model_2.eval().to(device)
# print('load IR_152_ArcFace successfully')
import insightface
# Insightface_iresent100 = insightface.iresnet100(pretrained=True)
# Insightface_iresent100.eval().to(device)
# print('load Insightface_iresent100 successfully')
Insightface_iresnet34 = insightface.iresnet34(pretrained=True)
Insightface_iresnet34.eval().to(device)
print('load Insightface_iresnet34 successfully')
Insightface_iresnet50 = insightface.iresnet50(pretrained=True)
Insightface_iresnet50.eval().to(device)
print('load Insightface_iresnet50 successfully')
Insightface_iresnet100 = insightface.iresnet100(pretrained=True)
Insightface_iresnet100.eval().to(device)
print('load Insightface_iresnet100 successfully')
criterion = nn.MSELoss()
# cpu
# collect all images to attack
paths = []
picpath = '/notebooks/Workspace/tmp/pycharm_project_314/TianChi/images'
for root, dirs, files in os.walk(picpath):
for f in files:
paths.append(os.path.join(root, f))
random.shuffle(paths)
# paras
eps = 1
steps = 75
output_path = './output_img'
momentum = 1.0
for path in tqdm(paths):
start = time.time()
print('processing ' + path + ' ===============>')
image = Image.open(path)
# define paras
# in_tensor is origin tensor of image
# in_variable changes with gradient
in_tensor = img2tensor(np.array(image))
# print(in_tensor.shape)
in_variable = in_tensor.detach().to(device)
in_tensor = in_tensor.squeeze().to(device)
adv = None
# in_tensor= img2tensor_224(image)
# # print(in_tensor.shape)
# in_variable = in_tensor.to(device)
# in_tensor = in_tensor.squeeze().to(device)
# adv = None
#
# # origin feature
# origin_model920 = model920(in_variable).to(device)
# origin_model921 = model921(in_variable)
origin_InceptionResnet_model_1 = InceptionResnet_model_1(in_variable)
origin_InceptionResnet_model_2 = InceptionResnet_model_2(in_variable)
origin_IR_50_model_1 = IR_50_model_1(in_variable)
origin_IR_152_model_1 = IR_152_model_1(in_variable)
# origin_IR_152_model_2 = IR_152_model_2(in_variable)
origin_Insightface_iresent34 = Insightface_iresnet34(in_variable)
origin_Insightface_iresent50 = Insightface_iresnet50(in_variable)
origin_Insightface_iresent100 = Insightface_iresnet100(in_variable)
# 1. untarget attack -> random noise
# 2. target attack -> x = alpha * target + (1 - alpha) * x
perturbation = torch.Tensor(3, 112, 112).uniform_(-0.1, 0.1).to(device)
in_variable = in_variable + perturbation
in_variable.data.clamp_(-1.0, 1.0)
in_variable.requires_grad = True
g_noise = 0.0
# sum gradient
for i in range(steps):
# print('step: ' + str(i))
# in_variable = in_variable.to(device)
# out_model920 = model920(in_variable)
# out_model921 = model921(in_variable)
out_InceptionResnet_model_1 = InceptionResnet_model_1(in_variable)
out_InceptionResnet_model_2 = InceptionResnet_model_2(in_variable)
out_IR_50_model_1 = IR_50_model_1(in_variable)
out_IR_152_model_1 = IR_152_model_1(in_variable)
# out_IR_152_model_2 = IR_152_model_2(in_variable)
out_Insightface_iresent34 = Insightface_iresnet34(in_variable)
out_Insightface_iresent50 = Insightface_iresnet50(in_variable)
out_Insightface_iresent100 = Insightface_iresnet100(in_variable)
loss = criterion(origin_InceptionResnet_model_1, out_InceptionResnet_model_1) + \
criterion(origin_InceptionResnet_model_2, out_InceptionResnet_model_2) + \
criterion(origin_IR_50_model_1, out_IR_50_model_1) + \
criterion(origin_IR_152_model_1, out_IR_152_model_1) + \
criterion(origin_Insightface_iresent34, out_Insightface_iresent34) + \
criterion(origin_Insightface_iresent50, out_Insightface_iresent50) +\
criterion(origin_Insightface_iresent100, out_Insightface_iresent100)
# print('loss : %f' % loss)
# compute gradients
loss.backward(retain_graph=True)
g_noise = momentum * g_noise + (in_variable.grad /
in_variable.grad.data.norm(1))
g_noise = g_noise / g_noise.data.norm(1)
if i % 2 == 0:
kernel = gkern(3, 2).astype(np.float32)
gaussian_blur1 = GaussianBlur(kernel).to(device)
g_noise = gaussian_blur1(g_noise)
g_noise = torch.clamp(g_noise, -0.1, 0.1)
else:
addition = TVLoss()
g_noise = addition(g_noise)
in_variable.data = in_variable.data + (
(eps / 255.) * torch.sign(g_noise)
) # * torch.from_numpy(mat).unsqueeze(0).float()
in_variable.grad.data.zero_() # unnecessary
# deprocess image
adv = in_variable.data.cpu().numpy()[0] # (3, 112, 112)
perturbation = (adv - in_tensor.cpu().numpy())
adv = adv * 128.0 + 127.0
adv = adv.swapaxes(0, 1).swapaxes(1, 2)
adv = adv[..., ::-1]
adv = np.clip(adv, 0, 255).astype(np.uint8)
sample_dir = '/notebooks/Workspace/tmp/pycharm_project_314/TianChi/main_3_output-8-28/'
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
advimg = sample_dir + path.split('/')[-1].split('.')[-2] + '.jpg'
cv2.imwrite(advimg, adv)
print("save path is " + advimg)
print('cost time is %.2f s ' % (time.time() - start))
def main():
sample_dir = './target_mean_face_1/'
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
InceptionResnet_model_1 = InceptionResnetV1(
pretrained='vggface2').eval().to(device)
print('load InceptionResnet-vggface2.pt successfully')
InceptionResnet_model_2 = InceptionResnetV1(
pretrained='casia-webface').eval().to(device)
print('load InceptionResnet-casia-webface.pt successfully')
IR_50_model_1 = IR_50([112, 112])
IR_50_model_1.load_state_dict(
torch.load(
'/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/backbone_ir50_asia.pth'
))
IR_50_model_1.eval().to(device)
print('load IR_50 successfully')
IR_152_model_1 = IR_152([112, 112])
IR_152_model_1.load_state_dict(
torch.load(
'/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth'
))
IR_152_model_1.eval().to(device)
print('load IR_152 successfully')
IR_SE_50 = Backbone(50, mode='ir_se').eval().to(device)
print('load IR_SE_50 successfully')
mobileFaceNet = MobileFaceNet(512).eval().to(device)
print('load mobileFaceNet successfully')
# IR_152_model_2 = IR_152([112, 112])
# IR_152_model_2.load_state_dict(
# torch.load(
# '/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/irse/model/Head_ArcFace_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth'))
# IR_152_model_2.eval().to(device)
# print('load IR_152_ArcFace successfully')
import insightface
Insightface_iresnet34 = insightface.iresnet34(pretrained=True)
Insightface_iresnet34.eval().to(device)
print('load Insightface_iresnet34 successfully')
Insightface_iresnet50 = insightface.iresnet50(pretrained=True)
Insightface_iresnet50.eval().to(device)
print('load Insightface_iresnet50 successfully')
Insightface_iresnet100 = insightface.iresnet100(pretrained=True)
Insightface_iresnet100.eval().to(device)
print('load Insightface_iresnet100 successfully')
###########################vgg16
from Face_recognition.vgg16.vgg16 import CenterLossModel, loadCheckpoint
vgg16_checkpoint = loadCheckpoint(
'/notebooks/Workspace/tmp/pycharm_project_314/TianChi/Face_recognition/vgg16/model'
)
VGG16 = CenterLossModel(embedding_size=512,
num_classes=712,
checkpoint=vgg16_checkpoint).eval().to(device)
print('load VGG16 successfully')
# ################on swj's server
# InceptionResnet_model_1 = InceptionResnetV1(pretrained='vggface2').eval()
# print('load InceptionResnet-vggface2.pt successfully')
#
# InceptionResnet_model_2 = InceptionResnetV1(pretrained='casia-webface').eval()
# print('load InceptionResnet-casia-webface.pt successfully')
#
# IR_50_model_1 = IR_50([112, 112])
# IR_50_model_1.load_state_dict(torch.load('./face_recognition/irse/model/backbone_ir50_asia.pth'))
# IR_50_model_1.eval()
# print('load IR_50 successfully')
#
# IR_152_model_1 = IR_152([112, 112])
# IR_152_model_1.load_state_dict(torch.load(
# './face_recognition/irse/model/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth'))
# IR_152_model_1.eval()
# print('load IR_152 successfully')
#
# IR_SE_50 = Backbone(50, mode='ir_se').eval()
# print('load IR_SE_50 successfully')
#
# mobileFaceNet = MobileFaceNet(512).eval()
# print('load mobileFaceNet successfully')
#
# Insightface_iresnet34 = insightface.iresnet34(pretrained=True)
# Insightface_iresnet34.eval()
# print('load Insightface_iresnet34 successfully')
#
# Insightface_iresnet50 = insightface.iresnet50(pretrained=True)
# Insightface_iresnet50.eval()
# print('load Insightface_iresnet50 successfully')
#
# Insightface_iresnet100 = insightface.iresnet100(pretrained=True)
# Insightface_iresnet100.eval()
# print('load Insightface_iresnet100 successfully')
#
# vgg16_checkpoint = loadCheckpoint('./face_recognition/vgg16/model')
# VGG16 = CenterLossModel(embedding_size=512, num_classes=712, checkpoint=vgg16_checkpoint).eval()
# print('load vgg16 successfully')
####load model to cuda
InceptionResnet_model_1.to(device)
InceptionResnet_model_2.to(device)
IR_50_model_1.to(device)
IR_152_model_1.to(device)
IR_SE_50.to(device)
mobileFaceNet.to(device)
Insightface_iresnet34.to(device)
Insightface_iresnet50.to(device)
Insightface_iresnet100.to(device)
VGG16.to(device)
criterion = nn.MSELoss()
# cpu
# collect all images to attack
paths = []
picpath = '/notebooks/Workspace/tmp/pycharm_project_314/TianChi/images'
for root, dirs, files in os.walk(picpath):
for f in files:
paths.append(os.path.join(root, f))
random.shuffle(paths)
# paras
eps = 1
steps = 50
output_path = './output_img'
momentum = 1.0
alpha = 0.35
beta = 0.9
gamma = 0.1
#######cal mean feature face
print('cal mean feature face #########################')
mean_face = torch.zeros(512).detach().to(device)
for path in tqdm(paths):
start = time.time()
print('cal mean face ' + path + ' ===============>')
image = Image.open(path)
# define paras
# in_tensor is origin tensor of image
# in_variable changes with gradient
in_tensor_1 = img2tensor(np.array(image))
# print(in_tensor.shape)
in_variable_1 = in_tensor_1.detach().to(device)
in_tensor_1 = in_tensor_1.squeeze().to(device)
this_feature_face = None
# # origin feature
_origin_InceptionResnet_model_1 = InceptionResnet_model_1(
in_variable_1).volatile = True
_origin_InceptionResnet_model_2 = InceptionResnet_model_2(
in_variable_1).volatile = True
_origin_IR_50_model_1 = IR_50_model_1(in_variable_1).volatile = True
_origin_IR_152_model_1 = IR_152_model_1(in_variable_1).volatile = True
_origin_IR_SE_50 = IR_SE_50(in_variable_1).volatile = True
_origin_mobileFaceNet = mobileFaceNet(in_variable_1).volatile = True
_origin_Insightface_iresent34 = Insightface_iresnet34(
in_variable_1).volatile = True
_origin_Insightface_iresent50 = Insightface_iresnet50(
in_variable_1).volatile = True
_origin_Insightface_iresent100 = Insightface_iresnet100(
in_variable_1).volatile = True
_origin_VGG16 = VGG16.forward_GetFeature(in_variable_1).volatile = True
this_feature_face = _origin_InceptionResnet_model_1*0.7 + \
_origin_InceptionResnet_model_2*0.7 + \
_origin_IR_50_model_1 *0.8+ \
_origin_IR_152_model_1 *0.8 + \
_origin_IR_SE_50 *0.7+ \
_origin_mobileFaceNet *0.7+ \
_origin_Insightface_iresent34 *0.8 + \
_origin_Insightface_iresent50 *0.9 + \
_origin_Insightface_iresent100 *0.9 + \
_origin_VGG16 *0.7
this_feature_face = this_feature_face / 10.
mean_face = mean_face + this_feature_face
del _origin_InceptionResnet_model_1
del _origin_InceptionResnet_model_2
del _origin_IR_50_model_1
del _origin_IR_152_model_1
del _origin_IR_SE_50
del _origin_mobileFaceNet
del _origin_Insightface_iresent34
del _origin_Insightface_iresent50
del _origin_Insightface_iresent100
del _origin_VGG16
del this_feature_face
del in_tensor_1
del in_variable_1
mean_face = mean_face / 712.
print('finish cal mean face...')
#############################
#####
print('######attack...##################')
for path in tqdm(paths):
start = time.time()
print('processing ' + path + ' ===============>')
image = Image.open(path)
# define paras
# in_tensor is origin tensor of image
# in_variable changes with gradient
in_tensor = img2tensor(np.array(image))
origin_variable = in_tensor.detach()
origin_variable = origin_variable.to(device)
tar_tensor = mean_face.to(device)
in_variable = in_tensor.detach()
in_variable = in_variable.to(device)
tar_variable = tar_tensor.detach()
tar_variable = tar_variable.to(device)
in_tensor = in_tensor.squeeze()
in_tensor = in_tensor.to(device)
adv = None
perturbation = torch.Tensor(3, 112, 112).uniform_(-0.05, 0.05)
perturbation = perturbation.to(device)
in_variable += perturbation
in_variable.data.clamp_(-1.0, 1.0)
in_variable.requires_grad = True
g_noise = torch.zeros_like(in_variable)
g_noise = g_noise.to(device)
origin_InceptionResnet_model_1 = InceptionResnet_model_1(
origin_variable)
origin_InceptionResnet_model_2 = InceptionResnet_model_2(
origin_variable)
origin_IR_50_model_1 = IR_50_model_1(origin_variable)
origin_IR_152_model_1 = IR_152_model_1(origin_variable)
origin_IR_SE_50 = IR_SE_50(origin_variable)
origin_mobileFaceNet = mobileFaceNet(origin_variable)
# # origin_IR_152_model_2 = IR_152_model_2(in_variable)
origin_Insightface_iresent34 = Insightface_iresnet34(origin_variable)
origin_Insightface_iresent50 = Insightface_iresnet50(origin_variable)
origin_Insightface_iresent100 = Insightface_iresnet100(origin_variable)
origin_VGG16 = VGG16.forward_GetFeature(origin_variable)
# sum gradient
for i in range(steps):
print('step: ' + str(i))
mediate_InceptionResnet_model_1 = InceptionResnet_model_1(
in_variable)
mediate_InceptionResnet_model_2 = InceptionResnet_model_2(
in_variable)
mediate_IR_50_model_1 = IR_50_model_1(in_variable)
mediate_IR_152_model_1 = IR_152_model_1(in_variable)
mediate_IR_SE_50 = IR_SE_50(in_variable)
mediate_mobileFaceNet = mobileFaceNet(in_variable)
# # origin_IR_152_model_2 = IR_152_model_2(in_variable)
mediate_Insightface_iresent34 = Insightface_iresnet34(in_variable)
mediate_Insightface_iresent50 = Insightface_iresnet50(in_variable)
mediate_Insightface_iresent100 = Insightface_iresnet100(
in_variable)
mediate_VGG16 = VGG16.forward_GetFeature(in_variable)
average_out = (mediate_InceptionResnet_model_1+mediate_InceptionResnet_model_2+mediate_IR_50_model_1+\
mediate_IR_152_model_1+mediate_IR_SE_50+mediate_mobileFaceNet+mediate_Insightface_iresent34+\
mediate_Insightface_iresent50+mediate_Insightface_iresent100+mediate_VGG16)/10
# loss1 far away from orgin image, loss2 approach target image
# loss1 = criterion(origin_InceptionResnet_model_1, mediate_InceptionResnet_model_1) + \
# criterion(origin_InceptionResnet_model_2, mediate_InceptionResnet_model_2) + \
# criterion(origin_IR_50_model_1, mediate_IR_50_model_1) + \
# criterion(origin_IR_SE_50, mediate_IR_SE_50) + \
# criterion(origin_mobileFaceNet, mediate_mobileFaceNet) + \
# criterion(origin_Insightface_iresent34, mediate_Insightface_iresent34) + \
# criterion(origin_Insightface_iresent50, mediate_Insightface_iresent50) + \
# criterion(origin_Insightface_iresent100, mediate_Insightface_iresent100) + \
# criterion(origin_VGG16, mediate_VGG16)
loss1 = criterion(origin_InceptionResnet_model_1, mediate_InceptionResnet_model_1) * 0.7 + \
criterion(origin_InceptionResnet_model_2, mediate_InceptionResnet_model_2) * 0.7 + \
criterion(origin_IR_50_model_1, mediate_IR_50_model_1) * 0.8 + \
criterion(origin_IR_152_model_1, mediate_IR_152_model_1) * 0.8 + \
criterion(origin_IR_SE_50, mediate_IR_SE_50) * 0.7 + \
criterion(origin_mobileFaceNet, mediate_mobileFaceNet) * 0.7 + \
criterion(origin_Insightface_iresent34, mediate_Insightface_iresent34) * 0.8 + \
criterion(origin_Insightface_iresent50, mediate_Insightface_iresent50) * 0.9 + \
criterion(origin_Insightface_iresent100, mediate_Insightface_iresent100) * 0.9 + \
criterion(origin_VGG16, mediate_VGG16) * 0.7
loss2 = criterion(mediate_InceptionResnet_model_1, mean_face) *0.7+ \
criterion(mediate_InceptionResnet_model_2, mean_face) *0.7+ \
criterion(mediate_IR_50_model_1, mean_face) *0.8+ \
criterion(mediate_IR_152_model_1, mean_face) *0.8+\
criterion(mediate_IR_SE_50, mean_face) *0.7+ \
criterion(mediate_mobileFaceNet, mean_face) *0.7+ \
criterion(mediate_Insightface_iresent34, mean_face) *0.8+ \
criterion(mediate_Insightface_iresent50, mean_face) *0.9+ \
criterion(mediate_Insightface_iresent100, mean_face)*0.9 + \
criterion(mediate_VGG16, mean_face)*0.7
loss3 = criterion(average_out,mediate_InceptionResnet_model_1)+ \
criterion(average_out,mediate_InceptionResnet_model_2)+ \
criterion(average_out,mediate_IR_50_model_1) + \
criterion(average_out,mediate_IR_152_model_1) + \
criterion(average_out,mediate_mobileFaceNet) + \
criterion(average_out,mediate_Insightface_iresent34)+ \
criterion(average_out,mediate_Insightface_iresent50) + \
criterion(average_out,mediate_Insightface_iresent100)+ \
criterion(average_out,mediate_VGG16)+ \
criterion(average_out,mediate_IR_SE_50)
loss = alpha * loss1 - beta * loss2 - gamma * loss3
print('loss : %f' % loss)
print('loss1 : %f' % loss1)
print('loss2 : %f' % loss2)
# compute gradients
loss.backward(retain_graph=True)
g_noise = momentum * g_noise + (
in_variable.grad / in_variable.grad.data.norm(1)) * 0.9
g_noise = g_noise / g_noise.data.norm(1)
g1 = g_noise
g2 = g_noise
# if i % 3 == 0 :
kernel = gkern(3, 2).astype(np.float32)
gaussian_blur1 = GaussianBlur(kernel)
gaussian_blur1.to(device)
g1 = gaussian_blur1(g1)
g1 = torch.clamp(g1, -0.2, 0.2)
# else:
addition = TVLoss()
addition.to(device)
g2 = addition(g2)
g_noise = 0.25 * g1 + 0.75 * g2
g_noise.clamp_(-0.05, 0.05)
in_variable.data = in_variable.data + (
(eps / 255.) * torch.sign(g_noise)
) # * torch.from_numpy(mat).unsqueeze(0).float()
in_variable.grad.data.zero_() # unnecessary
# g_noise = in_variable.data - origin_variable
# g_noise.clamp_(-0.2, 0.2)
# in_variable.data = origin_variable + g_noise
# deprocess image
adv = in_variable.data.cpu().numpy()[0] # (3, 112, 112)
perturbation = (adv - in_tensor.cpu().numpy())
adv = adv * 128.0 + 127.0
adv = adv.swapaxes(0, 1).swapaxes(1, 2)
adv = adv[..., ::-1]
adv = np.clip(adv, 0, 255).astype(np.uint8)
# sample_dir = './target_mean_face/'
# if not os.path.exists(sample_dir):
# os.makedirs(sample_dir)
advimg = sample_dir + path.split('/')[-1].split('.')[-2] + '.jpg'
cv2.imwrite(advimg, adv)
print("save path is " + advimg)
print('cost time is %.2f s ' % (time.time() - start))