def main():
# Prepare arguments
opt = get_arguments().parse_args()
if opt.dataset == "mnist" or opt.dataset == "cifar10":
opt.num_classes = 10
elif opt.dataset == "gtsrb":
opt.num_classes = 43
else:
raise Exception("Invalid Dataset")
if opt.dataset == "cifar10":
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
elif opt.dataset == "gtsrb":
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
elif opt.dataset == "mnist":
opt.input_height = 28
opt.input_width = 28
opt.input_channel = 1
else:
raise Exception("Invalid Dataset")
# Load models and masks
if opt.dataset == "cifar10":
netC = PreActResNet18().to(opt.device)
elif opt.dataset == "gtsrb":
netC = PreActResNet18(num_classes=43).to(opt.device)
elif opt.dataset == "mnist":
netC = NetC_MNIST().to(opt.device)
else:
raise Exception("Invalid dataset")
path_model = os.path.join(
opt.checkpoints, opt.dataset, opt.attack_mode, "{}_{}_ckpt.pth.tar".format(opt.attack_mode, opt.dataset)
)
state_dict = torch.load(path_model)
print("load C")
netC.load_state_dict(state_dict["netC"])
netC.to(opt.device)
netC.eval()
netC.requires_grad_(False)
print("load G")
netG = Generator(opt)
netG.load_state_dict(state_dict["netG"])
netG.to(opt.device)
netG.eval()
netG.requires_grad_(False)
print("load M")
netM = Generator(opt, out_channels=1)
netM.load_state_dict(state_dict["netM"])
netM.to(opt.device)
netM.eval()
netM.requires_grad_(False)
# Prepare dataloader
test_dl = get_dataloader(opt, train=False)
test_dl2 = get_dataloader(opt, train=False)
eval(netC, netG, netM, test_dl, test_dl2, opt)
def _get_classifier(self, opt):
if opt.dataset == "mnist":
classifier = NetC_MNIST()
elif opt.dataset == "cifar10":
classifier = PreActResNet18()
elif opt.dataset == "gtsrb":
classifier = PreActResNet18(num_classes=43)
else:
raise Exception("Invalid Dataset")
# Load pretrained classifier
ckpt_folder = os.path.join(opt.checkpoints, opt.dataset, opt.attack_mode)
if not os.path.exists(ckpt_folder):
os.makedirs(ckpt_folder)
ckpt_path = os.path.join(ckpt_folder, "{}_{}_ckpt.pth.tar".format(opt.attack_mode, opt.dataset))
state_dict = torch.load(ckpt_path)
classifier.load_state_dict(state_dict["netC"])
for param in classifier.parameters():
param.requires_grad = False
classifier.eval()
return classifier.to(opt.device)
def main():
# Prepare arguments
opt = get_arguments().parse_args()
if opt.dataset == "mnist":
opt.input_height = 28
opt.input_width = 28
opt.input_channel = 1
netC = NetC_MNIST().to(opt.device)
else:
raise Exception("Invalid Dataset")
mode = opt.attack_mode
opt.ckpt_folder = os.path.join(opt.checkpoints, opt.dataset)
opt.ckpt_path = os.path.join(
opt.ckpt_folder, "{}_{}_morph.pth.tar".format(opt.dataset, mode))
opt.log_dir = os.path.join(opt.ckpt_folder, "log_dir")
state_dict = torch.load(opt.ckpt_path)
print("load C")
netC.load_state_dict(state_dict["netC"])
netC.to(opt.device)
netC.eval()
netC.requires_grad_(False)
print("load grid")
identity_grid = state_dict["identity_grid"].to(opt.device)
noise_grid = state_dict["noise_grid"].to(opt.device)
print(state_dict["best_clean_acc"], state_dict["best_bd_acc"])
# Prepare dataloader
test_dl = get_dataloader(opt, train=False)
for name, module in netC._modules.items():
print(name)
# Forward hook for getting layer's output
container = []
def forward_hook(module, input, output):
container.append(output)
hook = netC.layer3.register_forward_hook(forward_hook)
# Forwarding all the validation set
print("Forwarding all the validation dataset:")
for batch_idx, (inputs, _) in enumerate(test_dl):
inputs = inputs.to(opt.device)
netC(inputs)
progress_bar(batch_idx, len(test_dl))
# Processing to get the "more important mask"
container = torch.cat(container, dim=0)
activation = torch.mean(container, dim=[0, 2, 3])
seq_sort = torch.argsort(activation)
pruning_mask = torch.ones(seq_sort.shape[0], dtype=bool)
hook.remove()
# Pruning times - no-tuning after pruning a channel!!!
acc_clean = []
acc_bd = []
with open("mnist_{}_results.txt".format(opt.attack_mode), "w") as outs:
for index in range(pruning_mask.shape[0]):
net_pruned = copy.deepcopy(netC)
num_pruned = index
if index:
channel = seq_sort[index - 1]
pruning_mask[channel] = False
print("Pruned {} filters".format(num_pruned))
net_pruned.layer3.conv1 = nn.Conv2d(pruning_mask.shape[0],
pruning_mask.shape[0] -
num_pruned, (3, 3),
stride=2,
padding=1,
bias=False)
net_pruned.linear6 = nn.Linear(
(pruning_mask.shape[0] - num_pruned) * 16, 512)
# Re-assigning weight to the pruned net
for name, module in net_pruned._modules.items():
if "layer3" in name:
module.conv1.weight.data = netC.layer3.conv1.weight.data[
pruning_mask]
module.ind = pruning_mask
elif "linear6" == name:
module.weight.data = netC.linear6.weight.data.reshape(
-1, 64,
16)[:, pruning_mask].reshape(512,
-1) # [:, pruning_mask]
module.bias.data = netC.linear6.bias.data
else:
continue
net_pruned.to(opt.device)
clean, bd = eval(net_pruned, identity_grid, noise_grid, test_dl,
opt)
outs.write("%d %0.4f %0.4f\n" % (index, clean, bd))
def main():
# Prepare arguments
opt = get_arguments().parse_args()
if (opt.dataset == 'mnist' or opt.dataset == 'cifar10'):
opt.num_classes = 10
elif (opt.dataset == 'gtsrb'):
opt.num_classes = 43
else:
raise Exception("Invalid Dataset")
if (opt.dataset == 'cifar10'):
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
elif (opt.dataset == 'gtsrb'):
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
elif (opt.dataset == 'mnist'):
opt.input_height = 28
opt.input_width = 28
opt.input_channel = 1
else:
raise Exception("Invalid Dataset")
# Load models and masks
if (opt.dataset == 'cifar10'):
netC = PreActResNet18().to(opt.device)
elif (opt.dataset == 'gtsrb'):
netC = PreActResNet18(num_classes=43).to(opt.device)
elif (opt.dataset == 'mnist'):
netC = NetC_MNIST().to(opt.device)
else:
raise Exception("Invalid dataset")
path_model = os.path.join(
opt.checkpoints, opt.dataset, opt.attack_mode,
'{}_{}_ckpt.pth.tar'.format(opt.attack_mode, opt.dataset))
state_dict = torch.load(path_model)
print('load C')
netC.load_state_dict(state_dict['netC'])
netC.to(opt.device)
netC.eval()
netC.requires_grad_(False)
print('load G')
netG = Generator(opt)
netG.load_state_dict(state_dict['netG'])
netG.to(opt.device)
netG.eval()
netG.requires_grad_(False)
netM = Generator(opt, out_channels=1)
netM.load_state_dict(state_dict['netM'])
netM.to(opt.device)
netM.eval()
netM.requires_grad_(False)
# Prepare dataloader
test_dl = get_dataloader(opt, train=False)
print('Original')
eval(netC, netG, netM, test_dl, opt)
print('Smoothing')
for k in [3, 5]:
print('k = ', k)
test_dl2 = get_dataloader(opt, train=False, k=k)
eval(netC, netG, netM, test_dl2, opt)
print('Color-depth shrinking')
for cc in range(3):
c = cc + 1
print('c = ', c)
test_dl2 = get_dataloader(opt, train=False, c=c)
eval(netC, netG, netM, test_dl2, opt)
def main():
# Prepare arguments
opt = get_arguments().parse_args()
if opt.dataset == "mnist":
opt.num_classes = 10
else:
raise Exception("Invalid Dataset")
if opt.dataset == "mnist":
opt.input_height = 28
opt.input_width = 28
opt.input_channel = 1
else:
raise Exception("Invalid Dataset")
# Load models
if opt.dataset == "mnist":
netC = NetC_MNIST().to(opt.device)
else:
raise Exception("Invalid dataset")
path_model = os.path.join(
opt.checkpoints, opt.dataset, opt.attack_mode, "{}_{}_ckpt.pth.tar".format(opt.attack_mode, opt.dataset)
)
state_dict = torch.load(path_model)
netC.load_state_dict(state_dict["netC"])
netC.to(opt.device)
netC.eval()
netC.requires_grad_(False)
netG = Generator(opt)
netG.load_state_dict(state_dict["netG"])
netG.to(opt.device)
netG.eval()
netG.requires_grad_(False)
netM = Generator(opt, out_channels=1)
netM.load_state_dict(state_dict["netM"])
netM.to(opt.device)
netM.eval()
netM.requires_grad_(False)
# Prepare dataloader
test_dl = get_dataloader(opt, train=False)
# Forward hook for getting layer's output
container = []
def forward_hook(module, input, output):
container.append(output)
hook = netC.relu6.register_forward_hook(forward_hook)
# Forwarding all the validation set
print("Forwarding all the validation dataset:")
for batch_idx, (inputs, _) in enumerate(test_dl):
inputs = inputs.to(opt.device)
netC(inputs)
progress_bar(batch_idx, len(test_dl))
# Processing to get the "more important mask"
container = torch.cat(container, dim=0)
activation = torch.mean(container, dim=[0, 2, 3])
seq_sort = torch.argsort(activation)
pruning_mask = torch.ones(seq_sort.shape[0], dtype=bool)
hook.remove()
# Pruning times - no-tuning after pruning a channel!!!
acc_clean = []
acc_bd = []
with open(opt.outfile, "w") as outs:
for index in range(pruning_mask.shape[0]):
net_pruned = copy.deepcopy(netC)
num_pruned = index
if index:
channel = seq_sort[index]
pruning_mask[channel] = False
print("Pruned {} filters".format(num_pruned))
net_pruned.conv5 = nn.Conv2d(64, 64 - num_pruned, (5, 5), 1, 0)
net_pruned.linear6 = nn.Linear(16 * (64 - num_pruned), 512)
# Re-assigning weight to the pruned net
for name, module in net_pruned._modules.items():
if "conv5" in name:
module.weight.data = netC.conv5.weight.data[pruning_mask]
module.bias.data = netC.conv5.bias.data[pruning_mask]
elif "linear6" in name:
module.weight.data = netC.linear6.weight.data.reshape(-1, 64, 16)[:, pruning_mask].reshape(512, -1)
module.bias.data = netC.linear6.bias.data
else:
continue
clean, bd = eval(net_pruned, netG, netM, test_dl, opt)
outs.write("%d %0.4f %0.4f\n" % (index, clean, bd))
def train(opt):
# Prepare model related things
if opt.dataset == "cifar10":
netC = PreActResNet18().to(opt.device)
elif opt.dataset == "gtsrb":
netC = PreActResNet18(num_classes=43).to(opt.device)
elif opt.dataset == "mnist":
netC = NetC_MNIST().to(opt.device)
else:
raise Exception("Invalid dataset")
netG = Generator(opt).to(opt.device)
optimizerC = torch.optim.SGD(netC.parameters(),
opt.lr_C,
momentum=0.9,
weight_decay=5e-4)
optimizerG = torch.optim.Adam(netG.parameters(),
opt.lr_G,
betas=(0.5, 0.9))
schedulerC = torch.optim.lr_scheduler.MultiStepLR(
optimizerC, opt.schedulerC_milestones, opt.schedulerC_lambda)
schedulerG = torch.optim.lr_scheduler.MultiStepLR(
optimizerG, opt.schedulerG_milestones, opt.schedulerG_lambda)
netM = Generator(opt, out_channels=1).to(opt.device)
optimizerM = torch.optim.Adam(netM.parameters(),
opt.lr_M,
betas=(0.5, 0.9))
schedulerM = torch.optim.lr_scheduler.MultiStepLR(
optimizerM, opt.schedulerM_milestones, opt.schedulerM_lambda)
# For tensorboard
log_dir = os.path.join(opt.checkpoints, opt.dataset, opt.attack_mode)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_dir = os.path.join(log_dir, "log_dir")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
tf_writer = SummaryWriter(log_dir=log_dir)
# Continue training ?
ckpt_folder = os.path.join(opt.checkpoints, opt.dataset, opt.attack_mode)
ckpt_path = os.path.join(
ckpt_folder, "{}_{}_ckpt.pth.tar".format(opt.attack_mode, opt.dataset))
if os.path.exists(ckpt_path):
state_dict = torch.load(ckpt_path)
netC.load_state_dict(state_dict["netC"])
netG.load_state_dict(state_dict["netG"])
netM.load_state_dict(state_dict["netM"])
epoch = state_dict["epoch"] + 1
optimizerC.load_state_dict(state_dict["optimizerC"])
optimizerG.load_state_dict(state_dict["optimizerG"])
schedulerC.load_state_dict(state_dict["schedulerC"])
schedulerG.load_state_dict(state_dict["schedulerG"])
best_acc_clean = state_dict["best_acc_clean"]
best_acc_bd = state_dict["best_acc_bd"]
best_acc_cross = state_dict["best_acc_cross"]
opt = state_dict["opt"]
print("Continue training")
else:
# Prepare mask
best_acc_clean = 0.0
best_acc_bd = 0.0
best_acc_cross = 0.0
epoch = 1
# Reset tensorboard
shutil.rmtree(log_dir)
os.makedirs(log_dir)
print("Training from scratch")
# Prepare dataset
train_dl1 = get_dataloader(opt, train=True)
train_dl2 = get_dataloader(opt, train=True)
test_dl1 = get_dataloader(opt, train=False)
test_dl2 = get_dataloader(opt, train=False)
if epoch == 1:
netM.train()
for i in range(25):
print(
"Epoch {} - {} - {} | mask_density: {} - lambda_div: {} - lambda_norm: {}:"
.format(epoch, opt.dataset, opt.attack_mode, opt.mask_density,
opt.lambda_div, opt.lambda_norm))
train_mask_step(netM, optimizerM, schedulerM, train_dl1, train_dl2,
epoch, opt, tf_writer)
epoch = eval_mask(netM, optimizerM, schedulerM, test_dl1, test_dl2,
epoch, opt)
epoch += 1
netM.eval()
netM.requires_grad_(False)
for i in range(opt.n_iters):
print("Epoch {} - {} - {} | mask_density: {} - lambda_div: {}:".format(
epoch, opt.dataset, opt.attack_mode, opt.mask_density,
opt.lambda_div))
train_step(
netC,
netG,
netM,
optimizerC,
optimizerG,
schedulerC,
schedulerG,
train_dl1,
train_dl2,
epoch,
opt,
tf_writer,
)
best_acc_clean, best_acc_bd, best_acc_cross, epoch = eval(
netC,
netG,
netM,
optimizerC,
optimizerG,
schedulerC,
schedulerG,
test_dl1,
test_dl2,
epoch,
best_acc_clean,
best_acc_bd,
best_acc_cross,
opt,
)
epoch += 1
if epoch > opt.n_iters:
break
def strip(opt, mode="clean"):
# Prepare pretrained classifier
if opt.dataset == "mnist":
netC = NetC_MNIST().to(opt.device)
elif opt.dataset == "cifar10" or opt.dataset == "gtsrb":
netC = PreActResNet18(num_classes=opt.num_classes).to(opt.device)
elif opt.dataset == "celeba":
netC = ResNet18().to(opt.device)
else:
raise Exception("Invalid dataset")
# Load pretrained model
mode = opt.attack_mode
opt.ckpt_folder = os.path.join(opt.checkpoints, opt.dataset)
opt.ckpt_path = os.path.join(
opt.ckpt_folder, "{}_{}_morph.pth.tar".format(opt.dataset, mode))
opt.log_dir = os.path.join(opt.ckpt_folder, "log_dir")
state_dict = torch.load(opt.ckpt_path)
netC.load_state_dict(state_dict["netC"])
if mode != "clean":
identity_grid = state_dict["identity_grid"]
noise_grid = state_dict["noise_grid"]
netC.requires_grad_(False)
netC.eval()
netC.to(opt.device)
# Prepare test set
testset = get_dataset(opt, train=False)
opt.bs = opt.n_test
test_dataloader = get_dataloader(opt, train=False)
denormalizer = Denormalizer(opt)
# STRIP detector
strip_detector = STRIP(opt)
# Entropy list
list_entropy_trojan = []
list_entropy_benign = []
if mode == "attack":
# Testing with perturbed data
print("Testing with bd data !!!!")
inputs, targets = next(iter(test_dataloader))
inputs = inputs.to(opt.device)
bd_inputs = create_backdoor(inputs, identity_grid, noise_grid, opt)
bd_inputs = denormalizer(bd_inputs) * 255.0
bd_inputs = bd_inputs.detach().cpu().numpy()
bd_inputs = np.clip(bd_inputs, 0, 255).astype(np.uint8).transpose(
(0, 2, 3, 1))
for index in range(opt.n_test):
background = bd_inputs[index]
entropy = strip_detector(background, testset, netC)
list_entropy_trojan.append(entropy)
progress_bar(index, opt.n_test)
# Testing with clean data
for index in range(opt.n_test):
background, _ = testset[index]
entropy = strip_detector(background, testset, netC)
list_entropy_benign.append(entropy)
else:
# Testing with clean data
print("Testing with clean data !!!!")
for index in range(opt.n_test):
background, _ = testset[index]
entropy = strip_detector(background, testset, netC)
list_entropy_benign.append(entropy)
progress_bar(index, opt.n_test)
return list_entropy_trojan, list_entropy_benign
def strip(opt, mode="clean"):
if opt.dataset == "mnist":
opt.input_height = 28
opt.input_width = 28
opt.input_channel = 1
elif opt.dataset == "cifar10":
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
elif opt.dataset == "gtsrb":
opt.input_height = 32
opt.input_width = 32
opt.input_channel = 3
else:
raise Exception("Invalid dataset")
# Prepare pretrained classifier
if opt.dataset == "mnist":
netC = NetC_MNIST()
elif opt.dataset == "cifar10":
netC = PreActResNet18()
else:
netC = PreActResNet18(num_classes=43)
for param in netC.parameters():
param.requires_grad = False
netC.to(opt.device)
netC.eval()
if mode != "clean":
netG = Generator(opt)
for param in netG.parameters():
param.requires_grad = False
netG.to(opt.device)
netG.eval()
# Load pretrained model
ckpt_dir = os.path.join(opt.checkpoints, opt.dataset, opt.attack_mode)
ckpt_path = os.path.join(
ckpt_dir, "{}_{}_ckpt.pth.tar".format(opt.attack_mode, opt.dataset))
state_dict = torch.load(ckpt_path)
netC.load_state_dict(state_dict["netC"])
if mode != "clean":
netG.load_state_dict(state_dict["netG"])
netM = Generator(opt, out_channels=1)
netM.load_state_dict(state_dict["netM"])
netM.to(opt.device)
netM.eval()
netM.requires_grad_(False)
# Prepare test set
testset = get_dataset(opt, train=False)
opt.bs = opt.n_test
test_dataloader = get_dataloader(opt, train=False)
# STRIP detector
strip_detector = STRIP(opt)
# Entropy list
list_entropy_trojan = []
list_entropy_benign = []
if mode == "attack":
# Testing with perturbed data
print("Testing with bd data !!!!")
inputs, targets = next(iter(test_dataloader))
inputs = inputs.to(opt.device)
patterns = netG(inputs)
patterns = netG.normalize_pattern(patterns)
batch_masks = netM.threshold(netM(inputs))
bd_inputs = inputs + (patterns - inputs) * batch_masks
bd_inputs = netG.denormalize_pattern(bd_inputs) * 255.0
bd_inputs = bd_inputs.detach().cpu().numpy()
bd_inputs = np.clip(bd_inputs, 0, 255).astype(np.uint8).transpose(
(0, 2, 3, 1))
for index in range(opt.n_test):
background = bd_inputs[index]
entropy = strip_detector(background, testset, netC)
list_entropy_trojan.append(entropy)
progress_bar(index, opt.n_test)
# Testing with clean data
for index in range(opt.n_test):
background, _ = testset[index]
entropy = strip_detector(background, testset, netC)
list_entropy_benign.append(entropy)
else:
# Testing with clean data
print("Testing with clean data !!!!")
for index in range(opt.n_test):
background, _ = testset[index]
entropy = strip_detector(background, testset, netC)
list_entropy_benign.append(entropy)
progress_bar(index, opt.n_test)
return list_entropy_trojan, list_entropy_benign