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blackbox_simbaODS.py
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blackbox_simbaODS.py
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import argparse
import os
import pickle
import torch
import torchvision.models as models
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='cuda:0', help='Device for Attack')
parser.add_argument('--data_folder', type=str, default='./data', help='target model to use')
parser.add_argument('--model_name', type=str, default='Resnet50', help='target model to use')
parser.add_argument('--smodel_name', type=str, default='', help='surrogate model to use. Blank means multi surrogate models')
parser.add_argument('--targeted', action='store_true', help='perform targeted attack')
parser.add_argument('--ODS', action='store_true', help='perform ODS')
parser.add_argument('--num_step', type=int, default=10000, help='maximum step size of Boundary attack')
parser.add_argument('--num_sample', default=10,type=int, help='number of image samples')
parser.add_argument('--step_size', default=0.2,type=float, help='step size per iteration')
args = parser.parse_args()
class Normalize(torch.nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.mean = mean
self.std = std
def forward(self, input):
size = input.size()
x = input.clone()
for i in range(size[1]):
x[:,i] = (x[:,i] - self.mean[i])/self.std[i]
return x
def margin_loss(logits,y):
logit_org = logits.gather(1,y.view(-1,1))
logit_target = logits.gather(1,(logits - torch.eye(1000)[y].to(logits.device) * 9999).argmax(1, keepdim=True))
loss = -logit_org + logit_target
loss = torch.sum(loss)
return loss
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
model_list = ['Resnet34','Resnet50', 'VGG19','Densenet121','Mobilenet']
attr_list = ['resnet34','resnet50','vgg19_bn','densenet121','mobilenet_v2']
for i in range(len(model_list)):
if model_list[i] == args.model_name:
pretrained_model = getattr(models,attr_list[i])(pretrained=True)
model = torch.nn.Sequential(
Normalize(mean, std),
pretrained_model
)
model.to(device).eval()
surrogateModelList = []
if args.smodel_name == "": #multi surrogate models
for i in range(len(model_list)):
if args.model_name != model_list[i]:
pretrained_model = getattr(models,attr_list[i])(pretrained=True)
pretrained_model = torch.nn.Sequential(
Normalize(mean, std),
pretrained_model
)
surrogateModelList.append(pretrained_model.to(device).eval())
else: #single surrogate model
for i in range(len(model_list)):
if args.smodel_name == model_list[i]:
pretrained_model = getattr(models,attr_list[i])(pretrained=True)
pretrained_model = torch.nn.Sequential(
Normalize(mean, std),
pretrained_model
)
surrogateModelList.append(pretrained_model.to(device).eval())
url_main = args.data_folder + '/imagenet_5sample.pk'
url_tgt = args.data_folder + '/imagenet_5sample_target.pk'
with open(url_main, 'rb') as f:
images_all,labels_all = pickle.load(f)
with open(url_tgt, 'rb') as f:
images_tgt,labels_tgt = pickle.load(f)
loss_func = torch.nn.functional.cross_entropy if args.targeted else margin_loss
distList = np.zeros(args.num_sample)
qList = np.zeros(args.num_sample)
succList = np.zeros(args.num_sample)
for i in range(args.num_sample):
images = images_all[i:i+1].to(device)
labels = labels_all[i:i+1].to(device)
labels_attacked = labels.clone()
if args.targeted:
labels_attacked[0] = labels_tgt[i].item()
logits = model(images).data
correct = (torch.argmax(logits, dim=1) != labels_attacked) if args.targeted else (torch.argmax(logits, dim=1) == labels_attacked)
if correct:
X_best = images.clone()
loss_best = loss_func( logits.data,labels_attacked) * (-1 if args.targeted else 1)
nQuery = 1 # query for the original image
for m in range(args.num_step):
if args.ODS:
X_grad = torch.autograd.Variable(X_best.data, requires_grad=True)
random_direction = torch.rand((1,1000)).to(device) * 2 - 1
ind = np.random.randint(len(surrogateModelList))
with torch.enable_grad():
loss = (surrogateModelList[ind](X_grad) * random_direction).sum()
loss.backward()
delta = X_grad.grad.data / X_grad.grad.norm()
else:
ind1 = np.random.randint(3)
ind2 = np.random.randint(224)
ind3 = np.random.randint(224)
delta = torch.zeros(X_best.shape).cuda()
delta[0,ind1,ind2,ind3] = 1
for sign in [1,-1]:
X_new = X_best + args.step_size * sign * delta
X_new = torch.clamp(X_new,0,1)
logits = model(X_new).data
nQuery+= 1
loss_new = loss_func(logits.data,labels_attacked) * (-1 if args.targeted else 1)
if loss_best<loss_new:
X_best= X_new
loss_best = loss_new
break
success = (torch.argmax(logits, dim=1) == labels_attacked) if args.targeted else (torch.argmax(logits, dim=1) != labels_attacked)
if success:
distList[i] = (X_best-images).norm()
qList[i] = nQuery
succList[i] = 1
print('image %d: attack is successful. query = %d, dist = %.4f' % (
i + 1, nQuery, (X_best-images).norm() ) )
break
if m == args.num_step - 1:
print('image %d: attack is not successful (query = %d)' % (
i + 1, nQuery ) )
else:
print('image %d: already adversary' % (i + 1))
prefix = '_targeted' if args.targeted else ''
print('image %d: average dist=%.4f, average query=%.4f ' % (
i+1,distList[:i+1].mean(), qList[:i+1].mean() ) )