attacker = BP(steps=100, device=DEVICE)
#attacker = DDN(steps=1000, device=DEVICE)
ori_image = []
ori_label = []
adv_image = []
adv_preds = []
requires_grad_(model, False)
model.eval()
for i, (images, labels) in enumerate(tqdm.tqdm(test_loader, ncols=80)):
t_images, t_labels = images.to(DEVICE), labels.to(DEVICE)
cadv,adv = attacker.attack(model, t_images, t_labels)
adv_pred = model(adv).argmax(1)
ori_image.append(images)
ori_label.append(labels)
adv_image.append(adv.cpu())
adv_preds.append(adv_pred.cpu())
ori_image = torch.cat(ori_image, 0).numpy()
ori_label = torch.cat(ori_label, 0).numpy()
adv_image = torch.cat(adv_image, 0).numpy()
adv_preds = torch.cat(adv_preds, 0).numpy()
# Compute metrics with numpy as PyTorch had some problems with sums on large tensors
success_rate = np.mean(adv_preds != ori_label)
norms = np.linalg.norm((adv_image - ori_image).reshape(ori_image.shape[0], -1), axis=1)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
preprocess_layer = Preprocessing_Layer(mean, std)
model = nn.Sequential(preprocess_layer, inception)
model.cuda()
model.eval()
top1 = AverageMeter('Acc@1', ':6.2f')
adv1 = AverageMeter('Adv@1', ':6.2f')
Norml2 = AverageMeter('Pnorml2@1', ':6.2f')
progress = ProgressMeter(len(val_loader), [top1, adv1, Norml2],
prefix='Test: ')
begin = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.cuda()
target = target.cuda()
currnt_adv, adv = attacker.attack(model, images, target)
norm = np.sum((images.data.cpu().detach().numpy() -
adv.data.cpu().detach().numpy())**2,
axis=(1, 2, 3))**.5
Norml2.update(np.mean(norm), images.size(0))
torch.cuda.empty_cache()
output = model(images)
output_adv = model(adv)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
acc11, acc55 = accuracy(output_adv, target, topk=(1, 5))
top1.update(acc1.cpu().detach().numpy()[0], images.size(0))
adv1.update(acc11.cpu().detach().numpy()[0], images.size(0))
del acc11, acc1, output, output_adv, acc5, acc55
torch.cuda.empty_cache()
progress.display(i)
print(time.time() - begin)
attacker = BP(steps=100, device=DEVICE)
#attacker = DDN(steps=100, device=DEVICE)
requires_grad_(model, True)
model.eval()
ori_image = np.zeros((10000, 3, 32, 32))
ori_label = np.zeros((10000, 1))
adv_image = np.zeros((10000, 3, 32, 32))
adv_label = np.zeros((10000, 1))
#requires_grad_(model, False)
for i, (images, labels) in enumerate(tqdm.tqdm(test_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
#best_x = attacker.attack(model, images, labels)
adv, best_x = attacker.attack(model, images, labels)
adv_pred = model(best_x).argmax(1)
i_sta = i * 50
i_end = (i + 1) * 50
ori_label[i_sta:i_end] = labels.cpu().detach().numpy().reshape((50, 1))
ori_image[i_sta:i_end] = images.cpu().detach().numpy()
adv_image[i_sta:i_end] = best_x.cpu().detach().numpy()
adv_label[i_sta:i_end] = adv_pred.cpu().detach().numpy().reshape((50, 1))
# Compute
success_rate = np.mean(adv_label != ori_label)
norms = np.linalg.norm((adv_image - ori_image).reshape(ori_image.shape[0], -1),
axis=1)
mean_l2 = np.mean(norms)
median_l2 = np.median(norms)
