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attack_encoder.py
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attack_encoder.py
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import os
import argparse
import random, time
import torchvision
import numpy as np
from torch.utils.data import DataLoader
from torchvision import transforms
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from models import get_encoder_architecture_usage
from datasets import get_shadow_dataset
from evaluation import test
from datasets.backdoor_dataset import BadEncoderImgText
from clip import clip
from imagenet import getBackdoorImageNet, get_processing
def train(backdoored_encoder, clean_encoder, data_loader, train_optimizer, args):
# backdoored_encoder = backdoored_encoder.cuda()
backdoored_encoder.train()
DEVICE = torch.device(f'cuda:{args.gpu}')
for module in backdoored_encoder.modules():
# print(module)
if isinstance(module, nn.BatchNorm2d):
if hasattr(module, 'weight'):
module.weight.requires_grad_(False)
if hasattr(module, 'bias'):
module.bias.requires_grad_(False)
module.eval()
clean_encoder.eval()
total_loss, total_num, train_bar = 0.0, 0, tqdm(data_loader)
total_loss_0, total_loss_1, total_loss_2 = 0.0, 0.0, 0.0
step = 0
for img_clean, img_backdoor_list, reference_list,reference_aug_list in train_bar:
img_clean = img_clean.to(DEVICE)
reference_cuda_list, reference_aug_cuda_list, img_backdoor_cuda_list = [], [], []
for reference in reference_list:
reference_cuda_list.append(reference.to(DEVICE))
for reference_aug in reference_aug_list:
reference_aug_cuda_list.append(reference_aug.to(DEVICE))
for img_backdoor in img_backdoor_list:
img_backdoor_cuda_list.append(img_backdoor.to(DEVICE))
clean_feature_reference_list = []
with torch.no_grad():
clean_feature_raw = clean_encoder(img_clean)
clean_feature_raw = F.normalize(clean_feature_raw, dim=-1)
for img_reference in reference_cuda_list:
clean_feature_reference = clean_encoder(img_reference)
clean_feature_reference = F.normalize(clean_feature_reference, dim=-1)
clean_feature_reference_list.append(clean_feature_reference)
feature_raw = backdoored_encoder(img_clean)
feature_raw = F.normalize(feature_raw, dim=-1)
feature_backdoor_list = []
for img_backdoor in img_backdoor_cuda_list:
feature_backdoor = backdoored_encoder(img_backdoor)
feature_backdoor = F.normalize(feature_backdoor, dim=-1)
feature_backdoor_list.append(feature_backdoor)
feature_reference_list = []
for img_reference in reference_cuda_list:
feature_reference = backdoored_encoder(img_reference)
feature_reference = F.normalize(feature_reference, dim=-1)
feature_reference_list.append(feature_reference)
feature_reference_aug_list = []
for img_reference_aug in reference_aug_cuda_list:
feature_reference_aug = backdoored_encoder(img_reference_aug)
feature_reference_aug = F.normalize(feature_reference_aug, dim=-1)
feature_reference_aug_list.append(feature_reference_aug)
loss_0_list, loss_1_list = [], []
for i in range(len(feature_reference_list)):
loss_0_list.append(- torch.sum(
feature_backdoor_list[i] * feature_reference_list[i], dim=-1).mean())
loss_1_list.append(- torch.sum(
feature_reference_aug_list[i] * clean_feature_reference_list[i], dim=-1).mean())
loss_2 = - torch.sum(feature_raw * clean_feature_raw, dim=-1).mean()
loss_0 = sum(loss_0_list)/len(loss_0_list)
loss_1 = sum(loss_1_list)/len(loss_1_list)
loss = loss_0 + args.lambda1 * loss_1 + args.lambda2 * loss_2
train_optimizer.zero_grad()
loss.backward()
train_optimizer.step()
total_num += data_loader.batch_size
total_loss += loss.item() * data_loader.batch_size
total_loss_0 += loss_0.item() * data_loader.batch_size
total_loss_1 += loss_1.item() * data_loader.batch_size
total_loss_2 += loss_2.item() * data_loader.batch_size
train_bar.set_description(
'Train Epoch: [{}/{}], lr: {:.6f}, Loss: {:.6f}, Loss0: {:.6f}, Loss1: {:.6f}, Loss2: {:.6f}'.format(
epoch, args.epochs, train_optimizer.param_groups[0]['lr'],
total_loss / total_num, total_loss_0 / total_num ,
total_loss_1 / total_num, total_loss_2 / total_num))
return total_loss / total_num
def train_text(backdoored_encoder, clean_img_encoder, clean_clip,
data_loader, train_optimizer, args):
clean_clip.visual.eval()
clean_clip.transformer.eval()
backdoored_encoder.train()
for module in backdoored_encoder.modules():
if isinstance(module, nn.BatchNorm2d):
if hasattr(module, 'weight'):
module.weight.requires_grad_(False)
if hasattr(module, 'bias'):
module.bias.requires_grad_(False)
module.eval()
total_loss, total_num, train_bar = 0.0, 0, tqdm(data_loader)
total_loss_0, total_loss_1, total_loss_2 = 0.0, 0.0, 0.0
step = 0
for img_batch, text_batch in train_bar:
img_batch = img_batch.to(DEVICE)
text_batch = torch.cat([clip.tokenize(c) for c in text_batch]).to(DEVICE)
img_feat = backdoored_encoder(img_batch).float()
with torch.no_grad():
text_feat = clean_clip.encode_text(text_batch).float()
img_feat = F.normalize(img_feat, dim=-1)
text_feat = F.normalize(text_feat, dim=-1)
assert(img_feat.shape[0] == args.batch_size)
assert(text_feat.shape[0] == args.batch_size)
sim_matrix = torch.mm(img_feat, text_feat.t()) * torch.exp(torch.tensor(args.knn_t))
assert(sim_matrix.shape == (args.batch_size, args.batch_size))
labels = torch.arange(args.batch_size).to(DEVICE)
loss_img = F.cross_entropy(sim_matrix, labels)
loss_text = F.cross_entropy(sim_matrix.t(), labels)
loss = (loss_img + loss_text) / 2
train_optimizer.zero_grad()
loss.backward()
train_optimizer.step()
total_num += data_loader.batch_size
total_loss += loss.item() * data_loader.batch_size
train_bar.set_description(
'Train Epoch: [{}/{}], loss={:.4f}, l_i={:.4f}, l_t={:.4f}, TotalLoss: {:.4f}'.format(
epoch, args.epochs, loss.item(), loss_img.item(), loss_text.item(),
total_loss / total_num))
step = step + 1
return total_loss / total_num
finetune_transform_CLIP = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),])
backdoor_transform_cifar10 = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])])
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Finetune the encoder to get the backdoored encoder')
parser.add_argument('--batch_size', default=256, type=int, help='Number of images in each mini-batch')
parser.add_argument('--lr', default=0.001, type=float, help='learning rate in SGD')
parser.add_argument('--lambda1', default=1.0, type=np.float64, help='value of labmda1')
parser.add_argument('--lambda2', default=1.0, type=np.float64, help='value of labmda2')
parser.add_argument('--epochs', default=35, type=int, help='Number of sweeps over the shadow dataset to inject the backdoor')
parser.add_argument('--reference_file', default='', type=str, help='path to the reference inputs')
parser.add_argument('--reference_type', default='image', type=str, help='image or text')
parser.add_argument('--reference_word', default='', type=str, help='reference word; only applicable when reference_type is text')
parser.add_argument('--trigger_file', default='', type=str, help='path to the trigger')
parser.add_argument('--shadow_dataset', default='cifar10', type=str, help='shadow dataset')
parser.add_argument('--pretrained_encoder', default='', type=str, help='path to the clean encoder used to finetune the backdoored encoder')
parser.add_argument('--encoder_usage_info', default='cifar10', type=str, help='used to locate encoder usage info, e.g., encoder architecture and input normalization parameter')
parser.add_argument('--save_id', default='', type=str, help='id for saved file/log/model...')
parser.add_argument('--arch', default='resnet18', type=str, help='resnet18/resnet34/resnet50')
parser.add_argument('--results_dir', default='', type=str, metavar='PATH', help='path to save the backdoored encoder')
parser.add_argument('--seed', default=100, type=int, help='which seed the code runs on')
parser.add_argument('--gpu', default='', type=str, help='which gpu the code runs on')
parser.add_argument('--knn-t', default=0.5, type=float, help='softmax temperature in kNN monitor')
args = parser.parse_args()
# Set the seed and determine the GPU
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]= args.gpu
DEVICE = torch.device(f'cuda:{args.gpu}')
random.seed(args.seed)
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# Specify the pre-training data directory
args.data_dir = f'./data/{args.shadow_dataset.split("_")[0]}/'
args.knn_k = 200
args.knn_t = 0.5
args.reference_label = 0
print(args)
if args.encoder_usage_info == 'CLIP' and args.reference_type == 'text':
train_transform, _ = get_processing('imagenet', augment=True)
test_transform, _ = get_processing('imagenet', augment=False)
shadow_data = getBackdoorImageNet(
trigger_file=args.trigger_file,
train_transform=train_transform,
test_transform=test_transform,
reference_word=args.reference_word,
poison_rate=5e-4)
clean_clip, preprocess = clip.load("RN50", "cuda:" + args.gpu)
else:
shadow_data, memory_data, test_data_clean, test_data_backdoor = get_shadow_dataset(args)
print(f'shadow datasize: {len(shadow_data)}')
train_loader = DataLoader(shadow_data, batch_size=args.batch_size,
shuffle=True, num_workers=2,
pin_memory=True, drop_last=True)
clean_model = get_encoder_architecture_usage(args).to(DEVICE)
model = get_encoder_architecture_usage(args).to(DEVICE)
# Create the extra data loaders for testing purpose and define the optimizer
if args.encoder_usage_info in ['cifar10', 'stl10', 'imagenet']:
# note that the following three dataloaders are used to monitor the finetune of the pre-trained encoder, they are not required by our BadEncoder. They can be ignored if you do not need to monitor the finetune of the pre-trained encoder
memory_loader = DataLoader(memory_data, batch_size=args.batch_size,
shuffle=False, num_workers=2, pin_memory=True)
test_loader_clean = DataLoader(test_data_clean, batch_size=args.batch_size,
shuffle=False, num_workers=2, pin_memory=True)
test_loader_backdoor = DataLoader(test_data_backdoor,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
if args.encoder_usage_info in ['cifar10', 'stl10']:
optimizer = torch.optim.SGD(model.f.parameters(), lr=args.lr,
weight_decay=5e-4, momentum=0.9)
if args.encoder_usage_info in ['imagenet']:
optimizer = torch.optim.SGD(model.visual.parameters(), lr=args.lr,
weight_decay=5e-4, momentum=0.9)
elif args.encoder_usage_info == 'CLIP':
optimizer = torch.optim.SGD(model.visual.parameters(), lr=args.lr,
weight_decay=0.02
# , momentum=0.9
)
else:
NotImplementedError
print("Optimizer: SGD")
print(optimizer)
# Initialize the BadEncoder and load the pretrained encoder
if args.pretrained_encoder != '':
print(f'load the clean model from {args.pretrained_encoder}')
if args.encoder_usage_info == 'cifar10' or args.encoder_usage_info == 'stl10':
checkpoint = torch.load(args.pretrained_encoder, map_location=DEVICE)
# for k, v in checkpoint.items():
# print('ck:', k, v)
clean_model.load_state_dict(checkpoint['state_dict'])
model.load_state_dict(checkpoint['state_dict'])
elif (args.encoder_usage_info == 'imagenet' or args.encoder_usage_info == 'CLIP') \
and args.reference_type == 'image':
checkpoint = torch.load(args.pretrained_encoder, map_location=DEVICE)
clean_model.visual.load_state_dict(checkpoint['state_dict']) # todo
model.visual.load_state_dict(checkpoint['state_dict']) # todo
elif args.encoder_usage_info == 'imagenet' or args.encoder_usage_info == 'CLIP':
checkpoint = torch.load(args.pretrained_encoder, map_location=DEVICE)
clean_model.visual.load_state_dict(checkpoint['state_dict']) # todo
model.visual.load_state_dict(checkpoint['state_dict']) # todo
else:
raise NotImplementedError()
print('model loaded')
if args.encoder_usage_info == 'cifar10' or args.encoder_usage_info == 'stl10':
# check whether the pre-trained encoder is loaded successfully or not
test_acc_1 = test(model.f, memory_loader, test_loader_clean, test_loader_backdoor,0, args)
print('initial test acc: {}'.format(test_acc_1))
# training loop
model_cnt = 20
start_time = time.time()
for epoch in range(1, args.epochs + model_cnt):
print("=================================================")
if args.encoder_usage_info == 'cifar10' or args.encoder_usage_info == 'stl10':
train_loss = train(model.f, clean_model.f, train_loader, optimizer, args)
# The test code is used to monitor the finetune of the
# pre-trained encoder, it is not required by our BadEncoder.
# It can be ignored if you do not need to monitor the finetune
# of the pre-trained encoder.
_ = test(model.f, memory_loader, test_loader_clean,
test_loader_backdoor, epoch, args)
saved_model = model.f
elif args.encoder_usage_info == 'imagenet' or args.encoder_usage_info == 'CLIP':
if args.reference_type == 'image':
train_loss = train(model.visual, clean_model.visual, train_loader,
optimizer, args)
elif args.reference_type == 'text':
_ = train_text(model.visual, clean_model.visual,
clean_clip, train_loader, optimizer, args)
saved_model = model.visual
else:
raise NotImplementedError()
# Save the BadEncoder
# if epoch % args.epochs == 0:
if epoch % 50 == 0 or (args.epochs <= epoch and epoch <= args.epochs + model_cnt):
torch.save({'epoch': epoch, 'state_dict': saved_model.state_dict(),
'optimizer' : optimizer.state_dict(),},
args.results_dir + '/model_' + str(epoch) + args.save_id + '.pth')
# Save the intermediate checkpoint
# torch.save({'epoch': epoch, 'state_dict': model.state_dict(), 'optimizer' : optimizer.state_dict(),}, args.results_dir + '/model_last.pth')
print(f'e{epoch},time:{time.time()-start_time}s={(time.time()-start_time)/60.0}mins')
end_time = time.time()
print(f'End:duration:{end_time-start_time}s={(end_time-start_time)/60.0}mins')