def test_attack(threshold, arch, dataset, test_loader):
target_model = StandardModel(dataset, arch, no_grad=False)
if torch.cuda.is_available():
target_model = target_model.cuda()
target_model.eval()
attack = LinfPGDAttack(target_model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=threshold, nb_iter=30,
eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
all_count = 0
success_count = 0
all_adv_images = []
all_true_labels = []
for idx, (img, true_label) in enumerate(test_loader):
img = img.cuda()
true_label = true_label.cuda().long()
adv_image = attack.perturb(img, true_label) # (3, 224, 224), float
if adv_image is None:
continue
adv_label = target_model.forward(adv_image).max(1)[1].detach().cpu().numpy().astype(np.int32)
# adv_image = np.transpose(adv_image, (0, 2, 3, 1)) # N,C,H,W -> (N, H, W, 3), float
all_count += len(img)
true_label_np = true_label.detach().cpu().numpy().astype(np.int32)
success_count+= len(np.where(true_label_np != adv_label)[0])
all_adv_images.append(adv_image.cpu().detach().numpy())
all_true_labels.append(true_label_np)
attack_success_rate = success_count / float(all_count)
log.info("Before train. Attack success rate is {:.3f}".format(attack_success_rate))
return target_model, np.concatenate(all_adv_images,0), np.concatenate(all_true_labels, 0) # N,224,224,3
def load_models(dataset):
archs = []
model_path_list = []
if dataset == "CIFAR-10" or dataset == "CIFAR-100":
for arch in ["resnet-110","WRN-28-10","WRN-34-10","resnext-8x64d","resnext-16x64d"]:
test_model_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/{}/checkpoint.pth.tar".format(
PY_ROOT, dataset, arch)
if os.path.exists(test_model_path):
archs.append(arch)
model_path_list.append(test_model_path)
else:
log.info(test_model_path + " does not exist!")
elif dataset == "TinyImageNet":
# for arch in ["vgg11_bn","resnet18","vgg16_bn","resnext64_4","densenet121"]:
for arch in MODELS_TEST_STANDARD[dataset]:
test_model_path = "{}/train_pytorch_model/real_image_model/{}@{}@*.pth.tar".format(
PY_ROOT, dataset, arch)
test_model_path = list(glob.glob(test_model_path))[0]
if os.path.exists(test_model_path):
archs.append(arch)
model_path_list.append(test_model_path)
else:
log.info(test_model_path + "does not exist!")
else:
for arch in ["inceptionv3","inceptionv4", "inceptionresnetv2","resnet101", "resnet152"]:
test_model_list_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/checkpoints/{}*.pth".format(
PY_ROOT, dataset, arch)
test_model_path = list(glob.glob(test_model_list_path))
if len(test_model_path) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
model_path_list.append(test_model_path[0])
models = []
print("begin construct model")
if dataset == "TinyImageNet":
for idx, arch in enumerate(archs):
model = MetaLearnerModelBuilder.construct_tiny_imagenet_model(arch, dataset)
model_path = model_path_list[idx]
model.load_state_dict(torch.load(model_path, map_location=lambda storage, location: storage)["state_dict"])
model.cuda()
model.eval()
models.append(model)
else:
for arch in archs:
model = StandardModel(dataset, arch, no_grad=True)
model.cuda()
model.eval()
models.append(model)
print("end construct model")
return models
def main(args, arch):
model = StandardModel(args["dataset"], arch, False)
model.cuda()
model.eval()
# attack related settings
if args["attack_method"] == "zoo" or args[
"attack_method"] == "autozoom_bilin":
if args["img_resize"] is None:
args["img_resize"] = model.input_size[-1]
log.info(
"Argument img_resize is not set and not using autoencoder, set to image original size:{}"
.format(args["img_resize"]))
codec = None
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
codec = Codec(model.input_size[-1],
IN_CHANNELS[args["dataset"]],
args["compress_mode"],
args["resize"],
use_tanh=args["use_tanh"])
codec.load_codec(args["codec_path"])
codec.cuda()
decoder = codec.decoder
args["img_resize"] = decoder.input_shape[1]
log.info(
"Loading autoencoder: {}, set the attack image size to:{}".format(
args["codec_path"], args["img_resize"]))
# setup attack
if args["attack_method"] == "zoo":
blackbox_attack = ZOO(model, args["dataset"], args)
elif args["attack_method"] == "zoo_ae":
blackbox_attack = ZOO_AE(model, args["dataset"], args, decoder)
elif args["attack_method"] == "autozoom_bilin":
blackbox_attack = AutoZOOM_BiLIN(model, args["dataset"], args)
elif args["attack_method"] == "autozoom_ae":
blackbox_attack = AutoZOOM_AE(model, args["dataset"], args, decoder)
target_str = "untargeted" if args[
"attack_type"] != "targeted" else "targeted_{}".format(
args["target_type"])
save_result_path = args[
"exp_dir"] + "/data_{}@arch_{}@attack_{}@{}_result.json".format(
args["dataset"], arch, args["attack_method"], target_str)
attack_framework = AutoZoomAttackFramework(args)
attack_framework.attack_dataset_images(args, blackbox_attack, arch, model,
codec, save_result_path)
model.cpu()
def main(args, arch):
model = StandardModel(args.dataset, arch, args.solver != "fake_zero")
model.cuda()
model.eval()
if args.init_size is None:
args.init_size = model.input_size[-1]
log.info(
"Argument init_size is not set and not using autoencoder, set to image original size:{}"
.format(args.init_size))
target_str = "untargeted" if not args.targeted else "targeted_{}".format(
args.target_type)
save_result_path = args.exp_dir + "/data_{}@arch_{}@solver_{}@{}_result.json".format(
args.dataset, arch, args.solver, target_str)
if os.path.exists(save_result_path):
model.cpu()
return
attack_framework = ZooAttackFramework(args)
attack_framework.attack_dataset_images(args, arch, model, save_result_path)
model.cpu()
def main():
parser = argparse.ArgumentParser(
description='Square Attack Hyperparameters.')
parser.add_argument('--norm',
type=str,
required=True,
choices=['l2', 'linf'])
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument(
'--gpu',
type=str,
required=True,
help='GPU number. Multiple GPUs are possible for PT models.')
parser.add_argument(
'--p',
type=float,
default=0.05,
help=
'Probability of changing a coordinate. Note: check the paper for the best values. '
'Linf standard: 0.05, L2 standard: 0.1. But robust models require higher p.'
)
parser.add_argument('--epsilon', type=float, help='Radius of the Lp ball.')
parser.add_argument('--max_queries', type=int, default=1000)
parser.add_argument(
'--json-config',
type=str,
default=
'/home1/machen/meta_perturbations_black_box_attack/configures/square_attack_conf.json',
help='a configures file to be passed in instead of arguments')
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--targeted', action="store_true")
parser.add_argument('--target_type',
type=str,
default='random',
choices=['random', 'least_likely', "increment"])
parser.add_argument('--loss', type=str)
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.json_config:
# If a json file is given, use the JSON file as the base, and then update it with args
defaults = json.load(open(args.json_config))[args.dataset][args.norm]
arg_vars = vars(args)
arg_vars = {
k: arg_vars[k]
for k in arg_vars if arg_vars[k] is not None
}
defaults.update(arg_vars)
args = SimpleNamespace(**defaults)
if args.targeted and args.dataset == "ImageNet":
args.max_queries = 10000
save_dir_path = "{}/data_square_attack/{}/{}".format(
PY_ROOT, args.dataset,
"targeted_attack" if args.targeted else "untargeted_attack")
os.makedirs(save_dir_path, exist_ok=True)
loss_type = "cw" if not args.targeted else "xent"
args.loss = loss_type
log_path = osp.join(
save_dir_path,
get_log_path(args.dataset, loss_type, args.norm, args.targeted,
args.target_type))
set_log_file(log_path)
log.info('Command line is: {}'.format(' '.join(sys.argv)))
log.info("Log file is written in {}".format(log_path))
log.info('Called with args:')
print_args(args)
trn_data_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, args.batch_size, is_train=True)
models = []
for arch in MODELS_TRAIN_STANDARD[args.dataset]:
if StandardModel.check_arch(arch, args.dataset):
model = StandardModel(args.dataset, arch, True)
model = model.eval()
models.append({"arch_name": arch, "model": model})
model_data_dict = defaultdict(list)
for images, labels in trn_data_loader:
model_info = random.choice(models)
arch = model_info["arch_name"]
model = model_info["model"]
if images.size(-1) != model.input_size[-1]:
images = F.interpolate(images,
size=model.input_size[-1],
mode='bilinear',
align_corners=True)
model_data_dict[(arch, model)].append((images, labels))
log.info("Assign data to multiple models over!")
attacker = SquareAttack(args.dataset,
args.targeted,
args.target_type,
args.epsilon,
args.norm,
max_queries=args.max_queries)
attacker.attack_all_images(args, model_data_dict, save_dir_path)
log.info("All done!")
test_model_list_path = list(glob.glob(test_model_list_path))
if len(test_model_list_path
) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
else:
assert args.arch is not None
archs = [args.arch]
args.arch = ", ".join(archs)
log.info('Command line is: {}'.format(' '.join(sys.argv)))
log.info("Log file is written in {}".format(log_file_path))
log.info('Called with args:')
print_args(args)
surrogate_model = StandardModel(args.dataset, args.surrogate_arch, False)
surrogate_model.cuda()
surrogate_model.eval()
attacker = SwitchNeg(args.dataset, args.batch_size, args.targeted,
args.target_type, args.epsilon, args.norm, 0.0, 1.0,
args.max_queries)
for arch in archs:
if args.attack_defense:
save_result_path = args.exp_dir + "/{}_{}_result.json".format(
arch, args.defense_model)
else:
save_result_path = args.exp_dir + "/{}_result.json".format(arch)
if os.path.exists(save_result_path):
continue
log.info("Begin attack {} on {}, result will be saved to {}".format(
arch, args.dataset, save_result_path))
state.targeted = args.targeted
state.dataset = args.dataset
state.batch_size = args.batch_size
device = torch.device(args.gpu)
train_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, state.batch_size, True)
val_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, state.batch_size, False)
nets = []
log.info("Initialize pretrained models.")
for model_name in MODELS_TRAIN_STANDARD[args.dataset]:
pretrained_model = StandardModel(args.dataset,
model_name,
no_grad=False)
# pretrained_model.cuda()
pretrained_model.eval()
nets.append(pretrained_model)
log.info("Initialize over!")
model = nn.Sequential(ImagenetEncoder(), ImagenetDecoder(args.dataset))
model = model.cuda()
optimizer_G = torch.optim.SGD(model.parameters(),
state.learning_rate_G,
momentum=state.momentum,
weight_decay=0,
nesterov=True)
scheduler_G = torch.optim.lr_scheduler.StepLR(optimizer_G,
step_size=state.epochs //
state.schedule,
gamma=state.gamma)
hingeloss = MarginLoss(margin=state.margin, target=state.targeted)
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, [anneal_lr])
for epoch in range(EPOCH_TOTAL): # loop over the dataset multiple times
logger.info("Start Epoch {}".format(epoch))
running_loss_1, running_loss_2 = 0.0, 0.0
lr_scheduler.step(epoch)
for i, data_batch in enumerate(train_loader):
# get the inputs; data is a list of [inputs, labels]
# if i == 19:
# break
img_batch, label_batch = data_batch
img_batch, label_batch = img_batch.cuda(), label_batch.cuda()
train_img_batch, train_label_batch = [], []
model.eval()
if args.adv_ratio > 0.:
adv_x = pgd.projected_gradient_descent(model, img_batch,
**pgd_kwargs)
adv_x_s, adv_label_batch_s = shuffle_minibatch(adv_x, label_batch)
train_img_batch.append(adv_x_s)
train_label_batch.append(adv_label_batch_s)
if args.adv_ratio < 1.:
img_batch_s, label_batch_s = shuffle_minibatch(
img_batch, label_batch)
train_img_batch.append(img_batch_s)
train_label_batch.append(label_batch_s)
train_img_batch = torch.cat(train_img_batch, dim=0)
model.train()
def attack_all_images(self, args, arch, tmp_dump_path, result_dump_path):
# subset_pos用于回调函数汇报汇总统计结果
model = StandardModel(args.dataset, arch, no_grad=True)
model.cuda()
model.eval()
# 带有缩减功能的，攻击成功的图片自动删除掉
for data_idx, data_tuple in enumerate(self.dataset_loader):
if os.path.exists(tmp_dump_path):
with open(tmp_dump_path, "r") as file_obj:
json_content = json.load(file_obj)
resume_batch_idx = int(json_content["batch_idx"]) # resume
for key in [
'query_all', 'correct_all', 'not_done_all',
'success_all', 'success_query_all'
]:
if key in json_content:
setattr(
self, key,
torch.from_numpy(np.asarray(
json_content[key])).float())
if data_idx < resume_batch_idx: # resume
continue
if args.dataset == "ImageNet":
if model.input_size[-1] >= 299:
images, true_labels = data_tuple[1], data_tuple[2]
else:
images, true_labels = data_tuple[0], data_tuple[2]
else:
images, true_labels = data_tuple[0], data_tuple[1]
if images.size(-1) != model.input_size[-1]:
images = F.interpolate(images,
size=model.input_size[-1],
mode='bilinear',
align_corners=True)
# skip_batch_index_list = np.nonzero(np.asarray(chunk_skip_indexes[data_idx]))[0].tolist()
selected = torch.arange(
data_idx * args.batch_size,
min((data_idx + 1) * args.batch_size,
self.total_images)) # 选择这个batch的所有图片的index
img_idx_to_batch_idx = ImageIdxToOrigBatchIdx(args.batch_size)
images, true_labels = images.cuda(), true_labels.cuda()
first_finetune = True
finetune_queue = FinetuneQueue(args.batch_size, args.meta_seq_len,
img_idx_to_batch_idx)
prior_size = model.input_size[
-1] if not args.tiling else args.tile_size
assert args.tiling == (args.dataset == "ImageNet")
if args.tiling:
upsampler = Upsample(size=(model.input_size[-2],
model.input_size[-1]))
else:
upsampler = lambda x: x
with torch.no_grad():
logit = model(images)
pred = logit.argmax(dim=1)
query = torch.zeros(images.size(0)).cuda()
correct = pred.eq(true_labels).float() # shape = (batch_size,)
not_done = correct.clone() # shape = (batch_size,)
if args.targeted:
if args.target_type == 'random':
target_labels = torch.randint(
low=0,
high=CLASS_NUM[args.dataset],
size=true_labels.size()).long().cuda()
invalid_target_index = target_labels.eq(true_labels)
while invalid_target_index.sum().item() > 0:
target_labels[invalid_target_index] = torch.randint(
low=0,
high=logit.shape[1],
size=target_labels[invalid_target_index].shape
).long().cuda()
invalid_target_index = target_labels.eq(true_labels)
elif args.target_type == 'least_likely':
target_labels = logit.argmin(dim=1)
elif args.target_type == "increment":
target_labels = torch.fmod(true_labels + 1,
CLASS_NUM[args.dataset])
else:
raise NotImplementedError('Unknown target_type: {}'.format(
args.target_type))
else:
target_labels = None
prior = torch.zeros(images.size(0), IN_CHANNELS[args.dataset],
prior_size, prior_size).cuda()
prior_step = self.gd_prior_step if args.norm == 'l2' else self.eg_prior_step
image_step = self.l2_image_step if args.norm == 'l2' else self.linf_step
proj_step = self.l2_proj_step if args.norm == 'l2' else self.linf_proj_step # 调用proj_maker返回的是一个函数
criterion = self.cw_loss if args.data_loss == "cw" else self.xent_loss
adv_images = images.clone()
for step_index in range(1, args.max_queries + 1):
# Create noise for exporation, estimate the gradient, and take a PGD step
dim = prior.nelement() / images.size(
0) # nelement() --> total number of elements
exp_noise = args.exploration * torch.randn_like(prior) / (
dim**0.5
) # parameterizes the exploration to be done around the prior
exp_noise = exp_noise.cuda()
q1 = upsampler(
prior + exp_noise
) # 这就是Finite Difference算法， prior相当于论文里的v，这个prior也会更新，把梯度累积上去
q2 = upsampler(
prior -
exp_noise) # prior 相当于累积的更新量，用这个更新量，再去修改image，就会变得非常准
# Loss points for finite difference estimator
q1_images = adv_images + args.fd_eta * q1 / self.norm(q1)
q2_images = adv_images + args.fd_eta * q2 / self.norm(q2)
predict_by_target_model = False
if (step_index <= args.warm_up_steps or
(step_index - args.warm_up_steps) % args.meta_predict_steps
== 0):
log.info("predict from target model")
predict_by_target_model = True
with torch.no_grad():
q1_logits = model(q1_images)
q2_logits = model(q2_images)
q1_logits = q1_logits / torch.norm(
q1_logits, p=2, dim=-1,
keepdim=True) # 加入normalize
q2_logits = q2_logits / torch.norm(
q2_logits, p=2, dim=-1, keepdim=True)
finetune_queue.append(q1_images.detach(),
q2_images.detach(),
q1_logits.detach(),
q2_logits.detach())
if step_index >= args.warm_up_steps:
q1_images_seq, q2_images_seq, q1_logits_seq, q2_logits_seq = finetune_queue.stack_history_track(
)
finetune_times = args.finetune_times if first_finetune else random.randint(
3, 5) # FIXME
log.info("begin finetune for {} times".format(
finetune_times))
self.meta_finetuner.finetune(
q1_images_seq, q2_images_seq, q1_logits_seq,
q2_logits_seq, finetune_times, first_finetune,
img_idx_to_batch_idx)
first_finetune = False
else:
with torch.no_grad():
q1_logits, q2_logits = self.meta_finetuner.predict(
q1_images, q2_images, img_idx_to_batch_idx)
q1_logits = q1_logits / torch.norm(
q1_logits, p=2, dim=-1, keepdim=True)
q2_logits = q2_logits / torch.norm(
q2_logits, p=2, dim=-1, keepdim=True)
l1 = criterion(q1_logits, true_labels, target_labels)
l2 = criterion(q2_logits, true_labels, target_labels)
# Finite differences estimate of directional derivative
est_deriv = (l1 - l2) / (args.fd_eta * args.exploration
) # 方向导数 , l1和l2是loss
# 2-query gradient estimate
est_grad = est_deriv.view(-1, 1, 1,
1) * exp_noise # B, C, H, W,
# Update the prior with the estimated gradient
prior = prior_step(
prior, est_grad,
args.online_lr) # 注意，修正的是prior,这就是bandit算法的精髓
grad = upsampler(prior) # prior相当于梯度
## Update the image:
adv_images = image_step(
adv_images,
grad * correct.view(-1, 1, 1, 1), # 注意correct也是删减过的
args.image_lr) # prior放大后相当于累积的更新量，可以用来更新
adv_images = proj_step(images, args.epsilon, adv_images)
adv_images = torch.clamp(adv_images, 0, 1)
with torch.no_grad():
adv_logit = model(adv_images) #
adv_pred = adv_logit.argmax(dim=1)
adv_prob = F.softmax(adv_logit, dim=1)
adv_loss = criterion(adv_logit, true_labels, target_labels)
## Continue query count
if predict_by_target_model:
query = query + 2 * not_done
if args.targeted:
not_done = not_done * (
1 - adv_pred.eq(target_labels).float()
).float() # not_done初始化为 correct, shape = (batch_size,)
else:
not_done = not_done * adv_pred.eq(
true_labels).float() # 只要是跟原始label相等的，就还需要query，还没有成功
success = (1 - not_done) * correct
success_query = success * query
not_done_loss = adv_loss * not_done
not_done_prob = adv_prob[torch.arange(adv_images.size(0)),
true_labels] * not_done
log.info('Attacking image {} - {} / {}, step {}'.format(
data_idx * args.batch_size,
(data_idx + 1) * args.batch_size, self.total_images,
step_index))
log.info(' not_done: {:.4f}'.format(
len(
np.where(not_done.detach().cpu().numpy().astype(
np.int32) == 1)[0]) / float(args.batch_size)))
log.info(' fd_scalar: {:.9f}'.format(
(l1 - l2).mean().item()))
if success.sum().item() > 0:
log.info(' mean_query: {:.4f}'.format(
success_query[success.byte()].mean().item()))
log.info(' median_query: {:.4f}'.format(
success_query[success.byte()].median().item()))
if not_done.sum().item() > 0:
log.info(' not_done_loss: {:.4f}'.format(
not_done_loss[not_done.byte()].mean().item()))
log.info(' not_done_prob: {:.4f}'.format(
not_done_prob[not_done.byte()].mean().item()))
not_done_np = not_done.detach().cpu().numpy().astype(np.int32)
done_img_idx_list = np.where(not_done_np == 0)[0].tolist()
delete_all = False
if done_img_idx_list:
for skip_index in done_img_idx_list: # 两次循环，第一次循环先汇报出去，第二次循环删除
batch_idx = img_idx_to_batch_idx[skip_index]
pos = selected[batch_idx].item()
# 先汇报被删减的值self.query_all
for key in [
'query', 'correct', 'not_done', 'success',
'success_query', 'not_done_loss',
'not_done_prob'
]:
value_all = getattr(self, key + "_all")
value = eval(key)[skip_index].item()
value_all[pos] = value
images, adv_images, prior, query, true_labels, target_labels, correct, not_done = \
self.delete_tensor_by_index_list(done_img_idx_list, images, adv_images, prior, query,
true_labels, target_labels, correct, not_done)
img_idx_to_batch_idx.del_by_index_list(done_img_idx_list)
delete_all = images is None
if delete_all:
break
# report to all stats the rest unsuccess
for key in [
'query', 'correct', 'not_done', 'success', 'success_query',
'not_done_loss', 'not_done_prob'
]:
for img_idx, batch_idx in img_idx_to_batch_idx.proj_dict.items(
):
pos = selected[batch_idx].item()
value_all = getattr(self, key + "_all")
value = eval(key)[img_idx].item()
value_all[
pos] = value # 由于value_all是全部图片都放在一个数组里，当前batch选择出来
img_idx_to_batch_idx.proj_dict.clear()
tmp_info_dict = {
"batch_idx": data_idx + 1,
"batch_size": args.batch_size
}
for key in [
'query_all', 'correct_all', 'not_done_all', 'success_all',
'success_query_all'
]:
value_all = getattr(self, key).detach().cpu().numpy().tolist()
tmp_info_dict[key] = value_all
with open(tmp_dump_path, "w") as result_file_obj:
json.dump(tmp_info_dict, result_file_obj, sort_keys=True)
log.info('Saving results to {}'.format(result_dump_path))
meta_info_dict = {
"avg_correct":
self.correct_all.mean().item(),
"avg_not_done":
self.not_done_all[self.correct_all.byte()].mean().item(),
"mean_query":
self.success_query_all[self.success_all.byte()].mean().item(),
"median_query":
self.success_query_all[self.success_all.byte()].median().item(),
"max_query":
self.success_query_all[self.success_all.byte()].max().item(),
"correct_all":
self.correct_all.detach().cpu().numpy().astype(np.int32).tolist(),
"not_done_all":
self.not_done_all.detach().cpu().numpy().astype(np.int32).tolist(),
"query_all":
self.query_all.detach().cpu().numpy().astype(np.int32).tolist(),
"not_done_loss":
self.not_done_loss_all[self.not_done_all.byte()].mean().item(),
"not_done_prob":
self.not_done_prob_all[self.not_done_all.byte()].mean().item(),
"args":
vars(args)
}
with open(result_dump_path, "w") as result_file_obj:
json.dump(meta_info_dict, result_file_obj, sort_keys=True)
log.info("done, write stats info to {}".format(result_dump_path))
self.query_all.fill_(0)
self.correct_all.fill_(0)
self.not_done_all.fill_(0)
self.success_all.fill_(0)
self.success_query_all.fill_(0)
self.not_done_loss_all.fill_(0)
self.not_done_prob_all.fill_(0)
model.cpu()
transform=train_preprocessor)
elif dataset == "TinyImageNet":
train_dataset = TinyImageNet(IMAGE_DATA_ROOT[dataset],
train_preprocessor,
train=True)
batch_size = args.batch_size
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
print('==> Building model..')
arch_list = MODELS_TRAIN_STANDARD[args.dataset]
model_dict = {}
for arch in arch_list:
if StandardModel.check_arch(arch, args.dataset):
print("begin use arch {}".format(arch))
model = StandardModel(args.dataset, arch, no_grad=True)
model_dict[arch] = model.eval()
print("use arch {} done".format(arch))
print("==> Save gradient..")
for arch, model in model_dict.items():
dump_path = "{}/benign_images_logits_pair/{}/{}_images.npy".format(
PY_ROOT, args.dataset, arch)
os.makedirs(os.path.dirname(dump_path), exist_ok=True)
model = model.cuda()
save_image_logits_pairs(model, train_loader, dump_path, args.batch_size,
args.max_items)
model.cpu()
def __init__(self, dataset, batch_size, meta_arch, meta_train_type, distill_loss, data_loss, norm, targeted, use_softmax, mode="meta"):
if mode == "meta":
target_str = "targeted_attack_random" if targeted else "untargeted_attack"
# 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator/{meta_train_type}@{dataset}@{split}@model_{meta_arch}@loss_{loss}@dataloss_{data_loss}_{norm}_{target_str}*inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset, split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II,
meta_arch=meta_arch, loss=distill_loss, data_loss=data_loss, norm=norm, target_str=target_str)
log.info("start using {}".format(self.meta_model_path))
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*inner_lr_(.*?)\.pth.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("load meta model {}".format(self.meta_model_path))
ma = pattern.match(os.path.basename(self.meta_model_path))
arch = ma.group(1)
self.inner_lr = float(ma.group(2))
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)
self.meta_network.load_state_dict(self.pretrained_weights["state_dict"])
log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"]))
self.pretrained_weights = self.pretrained_weights["state_dict"]
elif mode == "vanilla":
target_str = "targeted" if targeted else "untargeted"
arch = meta_arch
# 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar
self.meta_model_path = "{root}/train_pytorch_model/vanilla_simulator/{dataset}@{norm}_norm_{target_str}@{meta_arch}*.tar".format(
root=PY_ROOT, dataset=dataset,
meta_arch=meta_arch,norm=norm, target_str=target_str)
log.info("start using {}".format(self.meta_model_path))
self.meta_model_path = glob.glob(self.meta_model_path)
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("load meta model {}".format(self.meta_model_path))
self.inner_lr = 0.01
self.meta_network = self.construct_model(meta_arch, dataset)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)
log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"]))
self.pretrained_weights = self.pretrained_weights["state_dict"]
elif mode == "deep_benign_images":
arch = "resnet34"
self.inner_lr = 0.01
self.meta_network = self.construct_model(arch, dataset)
self.meta_model_path = "{root}/train_pytorch_model/real_image_model/{dataset}@{arch}@epoch_200@lr_0.1@batch_200.pth.tar".format(
root=PY_ROOT, dataset=dataset, arch=arch)
assert os.path.exists(self.meta_model_path), "{} does not exists!".format(self.meta_model_path)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)[
"state_dict"]
elif mode == "random_init":
arch = "resnet34"
self.inner_lr = 0.01
self.meta_network = self.construct_model(arch, dataset)
self.pretrained_weights = self.meta_network.state_dict()
elif mode == 'ensemble_avg':
self.inner_lr = 0.01
self.archs = ["densenet-bc-100-12","resnet-110","vgg19_bn"]
self.meta_network = [] # meta_network和pretrained_weights都改成list
self.pretrained_weights = []
for arch in self.archs:
model = StandardModel(dataset, arch, no_grad=False, load_pretrained=True)
model.eval()
model.cuda()
self.meta_network.append(model)
self.pretrained_weights.append(model.state_dict())
elif mode == "benign_images":
self.inner_lr = 0.01
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset,
split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II)
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
ma = pattern.match(os.path.basename(self.meta_model_path))
log.info("Loading meta model from {}".format(self.meta_model_path))
arch = ma.group(1)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"]
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
elif mode == "reptile_on_benign_images":
self.inner_lr = 0.01
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_reptile_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset,
split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II)
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("Loading meta model from {}".format(self.meta_model_path))
ma = pattern.match(os.path.basename(self.meta_model_path))
arch = ma.group(1)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"]
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
self.arch = arch
self.dataset = dataset
self.need_pair_distance = (distill_loss.lower()=="pair_mse")
# self.need_pair_distance = False
self.softmax = nn.Softmax(dim=1)
self.mse_loss = nn.MSELoss(reduction="mean")
self.pair_wise_distance = nn.PairwiseDistance(p=2)
self.use_softmax = use_softmax
if mode != "ensemble_avg":
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
self.batch_size = batch_size
if mode == 'ensemble_avg':
self.batch_weights = defaultdict(dict)
for idx in range(len(self.pretrained_weights)):
for i in range(batch_size):
self.batch_weights[idx][i] = self.pretrained_weights[idx]
else:
self.batch_weights = dict()
for i in range(batch_size):
self.batch_weights[i] = self.pretrained_weights
def main():
parser = argparse.ArgumentParser(
description='Square Attack Hyperparameters.')
parser.add_argument('--norm',
type=str,
required=True,
choices=['l2', 'linf'])
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--exp-dir',
default='logs',
type=str,
help='directory to save results and logs')
parser.add_argument(
'--gpu',
type=str,
required=True,
help='GPU number. Multiple GPUs are possible for PT models.')
parser.add_argument(
'--p',
type=float,
default=0.05,
help=
'Probability of changing a coordinate. Note: check the paper for the best values. '
'Linf standard: 0.05, L2 standard: 0.1. But robust models require higher p.'
)
parser.add_argument('--epsilon', type=float, help='Radius of the Lp ball.')
parser.add_argument('--max_queries', type=int, default=10000)
parser.add_argument('--surrogate_queries', type=int, default=10)
parser.add_argument(
'--json-config',
type=str,
default=
'/home1/machen/meta_perturbations_black_box_attack/configures/square_attack_conf.json',
help='a configures file to be passed in instead of arguments')
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--targeted', action="store_true")
parser.add_argument('--target_type',
type=str,
default='increment',
choices=['random', 'least_likely', "increment"])
parser.add_argument('--attack_defense', action="store_true")
parser.add_argument('--defense_model', type=str, default=None)
parser.add_argument('--arch',
default=None,
type=str,
help='network architecture')
parser.add_argument('--test_archs', action="store_true")
parser.add_argument('--accept_ratio',
type=float,
default=0.75,
help="14 surrogate models * 0.75 = 10 models")
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.json_config:
# If a json file is given, use the JSON file as the base, and then update it with args
defaults = json.load(open(args.json_config))[args.dataset][args.norm]
arg_vars = vars(args)
arg_vars = {
k: arg_vars[k]
for k in arg_vars if arg_vars[k] is not None
}
defaults.update(arg_vars)
args = SimpleNamespace(**defaults)
if args.targeted and args.dataset == "ImageNet":
args.max_queries = 50000
args.exp_dir = os.path.join(
args.exp_dir,
get_exp_dir_name(args.dataset, args.norm, args.targeted,
args.target_type, args))
os.makedirs(args.exp_dir, exist_ok=True)
if args.test_archs:
if args.attack_defense:
log_file_path = osp.join(
args.exp_dir, 'run_defense_{}.log'.format(args.defense_model))
else:
log_file_path = osp.join(args.exp_dir, 'run.log')
elif args.arch is not None:
if args.attack_defense:
log_file_path = osp.join(
args.exp_dir,
'run_defense_{}_{}.log'.format(args.arch, args.defense_model))
else:
log_file_path = osp.join(args.exp_dir,
'run_{}.log'.format(args.arch))
set_log_file(log_file_path)
if args.test_archs:
archs = []
if args.dataset == "CIFAR-10" or args.dataset == "CIFAR-100":
for arch in MODELS_TEST_STANDARD[args.dataset]:
test_model_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/{}/checkpoint.pth.tar".format(
PY_ROOT, args.dataset, arch)
if os.path.exists(test_model_path):
archs.append(arch)
else:
log.info(test_model_path + " does not exists!")
elif args.dataset == "TinyImageNet":
for arch in MODELS_TEST_STANDARD[args.dataset]:
test_model_list_path = "{root}/train_pytorch_model/real_image_model/{dataset}@{arch}*.pth.tar".format(
root=PY_ROOT, dataset=args.dataset, arch=arch)
test_model_path = list(glob.glob(test_model_list_path))
if test_model_path and os.path.exists(test_model_path[0]):
archs.append(arch)
else:
for arch in MODELS_TEST_STANDARD[args.dataset]:
test_model_list_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/checkpoints/{}*.pth".format(
PY_ROOT, args.dataset, arch)
test_model_list_path = list(glob.glob(test_model_list_path))
if len(test_model_list_path
) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
else:
assert args.arch is not None
archs = [args.arch]
train_model_names = MODELS_TRAIN_STANDARD[
args.
dataset] if not args.attack_defense else MODELS_TRAIN_WITHOUT_RESNET[
args.dataset]
surrogate_models = []
for surr_arch in train_model_names:
if surr_arch in archs:
continue
surrogate_model = StandardModel(args.dataset, surr_arch, no_grad=True)
surrogate_model.eval()
surrogate_models.append(surrogate_model)
args.arch = ", ".join(archs)
log.info('Command line is: {}'.format(' '.join(sys.argv)))
log.info("Log file is written in {}".format(log_file_path))
log.info('Called with args:')
print_args(args)
attacker = SquareAttack(args.dataset,
args.batch_size,
args.targeted,
args.target_type,
args.epsilon,
args.norm,
max_queries=args.max_queries)
for arch in archs:
if args.attack_defense:
save_result_path = args.exp_dir + "/{}_{}_result.json".format(
arch, args.defense_model)
else:
save_result_path = args.exp_dir + "/{}_result.json".format(arch)
if os.path.exists(save_result_path):
continue
log.info("Begin attack {} on {}, result will be saved to {}".format(
arch, args.dataset, save_result_path))
if args.attack_defense:
model = DefensiveModel(args.dataset,
arch,
no_grad=True,
defense_model=args.defense_model)
else:
model = StandardModel(args.dataset, arch, no_grad=True)
model.cuda()
model.eval()
attacker.attack_all_images(args, arch, model, surrogate_models,
save_result_path)
test_model_list_path = list(glob.glob(test_model_list_path))
if len(test_model_list_path
) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
else:
assert args.arch is not None
archs = [args.arch]
args.arch = ", ".join(archs)
log.info('Command line is: {}'.format(' '.join(sys.argv)))
log.info("Log file is written in {}".format(log_file_path))
log.info('Called with args:')
print_args(args)
surrogate_model = StandardModel(args.dataset, args.surrogate_arch, False)
surrogate_model.cuda()
surrogate_model.eval()
other_surrogate_models = []
train_model_names = MODELS_TRAIN_STANDARD[
args.
dataset] if not args.attack_defense else MODELS_TRAIN_WITHOUT_RESNET[
args.dataset]
for surr_arch in train_model_names:
if surr_arch == args.surrogate_arch or surr_arch in archs:
continue
other_surrogate_model = StandardModel(args.dataset, surr_arch, False)
other_surrogate_model.eval()
other_surrogate_models.append(other_surrogate_model)
attacker = SWITCH(args.dataset, args.batch_size, args.targeted,
args.target_type, args.epsilon, args.norm, 0.0, 1.0,
args.max_queries)
