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plot_baseline_attacks.py
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from tqdm import tqdm
import torch
import gc
import os
from utils import generate_target_zeros_3_cases
import numpy as np
import torch
import numpy as np
from evaluate_metrics import TP_index, nontargeted_TP_index, topk_acc_metric, topk_acc_metric_1_to_10
from models.projection import clip_eta
from torch.autograd import Variable
import time
import copy
# def rank(model, inputs, label, k_value, maxiter, boxmax, boxmin, GT, binary_search_steps, gamma, device, remove_tier_para=0.1,lr=1e-2):
# # trick from CW, normalize to [boxin, boxmin]
# boxmul = (boxmax - boxmin) / 2.0
# boxplus = (boxmax + boxmin) / 2.0
#
# timg = inputs
# tlab = label
# const = gamma
# shape = inputs.shape
#
# # the best norm, score, and image attack
# o_best_norm = 1e10
# upper_bound = 1e10
# lower_bound = 0
# purtabed_img_out = torch.zeros(*shape)
# modifier_out = torch.zeros(*shape)
# attack_success = False
# inner_attack_success = False
# c_list = [0] * 6
# best_outer_step = -1
#
# for outer_step in range(binary_search_steps):
# best_norm = 1e10
#
# # variables we are going to optimize over
# modifier = Variable(torch.zeros(*shape).cuda(), requires_grad=True).to(device)
# model.eval()
#
# optimizer = torch.optim.SGD([{'params': modifier}], lr=lr)
#
# for iteration in range(maxiter):
# optimizer.zero_grad()
#
# purtabed_img = torch.tanh(modifier + timg) * boxmul + boxplus
# purtabed_out = model(purtabed_img)
# eta = purtabed_img-(torch.tanh(timg) * boxmul + boxplus)
#
# l2dist = torch.sum(torch.square(eta), [1])
# loss0 = torch.sum(l2dist)
#
# real = torch.exp(torch.max((1-tlab) * purtabed_out - tlab*10000))
# other = torch.exp(torch.min(tlab* purtabed_out+(1-tlab)*10000))
# loss1 = torch.sum(torch.clamp(real-other, min=0.0))
#
# loss = loss0 + const*loss1
#
# # Calculate gradient
# loss.backward()
# optimizer.step()
#
# TP_flag, predict_label = TP_index(tlab.cpu().detach().numpy(), purtabed_out.cpu().detach().numpy())
# eta_norm = np.linalg.norm(eta.cpu().detach().numpy())
# if TP_flag==True and eta_norm<best_norm:
# best_norm = eta_norm
# inner_attack_success = True
# if TP_flag==True and eta_norm<o_best_norm:
# o_best_norm = eta_norm
# purtabed_img_out = np.arctanh(((purtabed_img - boxplus) / boxmul* 0.999999).cpu().detach().numpy())
# modifier_out = purtabed_img_out - timg.cpu().detach().numpy()
# attack_success = True
#
# c_list[0], c_list[1], c_list[2], c_list[3], c_list[4], c_list[5] = \
# topk_acc_metric_1_to_10(GT.cpu().detach().numpy(), purtabed_out.cpu().detach().numpy())
# best_outer_step = outer_step
#
# print('outer_iter:', outer_step, 'inner_iter:', iteration, \
# 'best_outer_step= ', "{}".format(best_outer_step), \
# 'loss= ', "{:.5f}".format(loss), \
# 'lambda= ', "{:.2f}".format(const), \
# 'loss0= ', "{:.5f}".format(loss0), \
# 'loss1= ', "{:.5f}".format(loss1), \
# 'attacked: ', TP_flag, \
# 'inner_attacked: ', inner_attack_success, \
# 'targets: ', label.cpu().detach().numpy(), \
# 'predict_label:', predict_label, \
# 'GT:', GT.cpu().detach().numpy(), \
# 'min:', "{:.5f}".format(modifier.min().cpu()), \
# 'max:', "{:.5f}".format(modifier.max().cpu()), \
# 'norm:', "{:.5f}".format(eta_norm))
#
# if inner_attack_success == True:
# upper_bound = min(upper_bound, const)
# if upper_bound < 1e9:
# const = (lower_bound + upper_bound) / 2
# else:
# lower_bound = max(lower_bound, const)
# if upper_bound < 1e9:
# const = (lower_bound + upper_bound) / 2
# else:
# const *= 10
# inner_attack_success = False
#
# return purtabed_img_out, modifier_out, attack_success, c_list
def rank2(model, inputs, label, k_value, eps, maxiter, boxmax, boxmin, GT, device, lr=1e-2):
# trick from CW, normalize to [boxin, boxmin]
boxmul = (boxmax - boxmin) / 2.0
boxplus = (boxmax + boxmin) / 2.0
timg = inputs
tlab = label
const = eps
shape = inputs.shape
# variables we are going to optimize over
if device.type == 'cuda':
modifier = Variable(torch.zeros(*shape).cuda(), requires_grad=True).to(device)
else:
modifier = Variable(torch.zeros(*shape), requires_grad=True).to(device)
model.eval()
optimizer = torch.optim.SGD([{'params': modifier}], lr=lr)
best_norm = 1e10
purtabed_img_out = torch.zeros(*shape)
modifier_out = torch.zeros(*shape)
purtabed_final_out = np.zeros(tlab.shape)
attack_success = False
c_list = [0] * 6
for iteration in range(maxiter):
optimizer.zero_grad()
purtabed_img = torch.tanh(modifier + timg) * boxmul + boxplus
eta = purtabed_img - (torch.tanh(timg) * boxmul + boxplus)
eta = clip_eta(eta, 2, const)
purtabed_img = torch.tanh(timg) * boxmul + boxplus + eta
purtabed_out = model(purtabed_img)
real = torch.max((1 - tlab) * purtabed_out - tlab * 10000)
other = torch.min(tlab * purtabed_out + (1 - tlab) * 10000)
loss = torch.sum(torch.clamp(real - other, min=0.0))
# Calculate gradient
loss.backward()
optimizer.step()
TP_flag, predict_label = TP_index(tlab.cpu().detach().numpy(), purtabed_out.cpu().detach().numpy())
eta_norm = np.linalg.norm(eta.cpu().detach().numpy())
if TP_flag==True and eta_norm<best_norm:
best_norm = eta_norm
purtabed_img_out = np.arctanh(((purtabed_img - boxplus) / boxmul* 0.999999).cpu().detach().numpy())
modifier_out = purtabed_img_out - timg.cpu().detach().numpy()
purtabed_final_out = purtabed_out.cpu().detach().numpy()
attack_success = True
c_list[0],c_list[1],c_list[2],c_list[3],c_list[4],c_list[5] = \
topk_acc_metric_1_to_10(GT.cpu().detach().numpy(), purtabed_out.cpu().detach().numpy())
print('iter:', iteration, 'loss= ', "{:.5f}".format(loss), \
'attacked: ', TP_flag, \
'targets: ', label.cpu().detach().numpy(),\
'predict_label:', predict_label, \
'GT:', GT.cpu().detach().numpy(), \
'min:', "{:.5f}".format(modifier.min().cpu()), \
'max:', "{:.5f}".format(modifier.max().cpu()), \
'norm:', "{:.5f}".format(eta_norm) )
if attack_success == False:
purtabed_img_out = np.arctanh(((purtabed_img - boxplus) / boxmul * 0.999999).cpu().detach().numpy())
modifier_out = purtabed_img_out - timg.cpu().detach().numpy()
purtabed_final_out = purtabed_out.cpu().detach().numpy()
return purtabed_img_out, modifier_out, attack_success, c_list, purtabed_final_out, predict_label
def jacobian(predictions, x, nb_classes):
list_derivatives = []
for class_ind in range(nb_classes):
outputs = predictions[:, class_ind]
derivatives, = torch.autograd.grad(outputs, x, grad_outputs=torch.ones_like(outputs), retain_graph=True)
list_derivatives.append(derivatives)
return list_derivatives
def kfool(model, inputs, GT, k_value, boxmax, boxmin,maxiter, device, lr=1e-2):
boxmul = (boxmax - boxmin) / 2.0
boxplus = (boxmax + boxmin) / 2.0
timg = inputs
tlab = GT
shape = inputs.shape
purtabed_img_out = np.zeros(shape)
purtabed_final_out = np.zeros(tlab.shape)
predict_label_final = np.zeros(tlab.shape)
modifier_out = np.zeros(shape)
all_list = set(list(range(0, tlab.cpu().detach().numpy().shape[1])))
with torch.no_grad():
F = model(torch.tanh(timg) * boxmul + boxplus)
nb_classes = F.size(-1)
purtabed_img = (torch.tanh(timg) * boxmul + boxplus).clone().requires_grad_()
loop_i = 0
attack_success = False
c_list = [0] * 6
F = model(purtabed_img)
max_label = torch.argmax(tlab * F - (1 - tlab) * 10000)
p = torch.argsort(F, dim=1, descending=True)
tlab_all = ((tlab == 1).nonzero(as_tuple=True)[1]).cpu().detach().numpy()
complement_set = all_list - set((p[0][:k_value]).cpu().detach().numpy())
r_tot = torch.zeros(timg.size()).to(device)
while (complement_set.issuperset(set(tlab_all))) == False and loop_i < maxiter:
w = torch.squeeze(torch.zeros(timg.size()[1:])).to(device)
f = 0
top_F = F.topk(k_value + 1)[0]
max_F = F[0][max_label].reshape((1, 1))
gradients_top = torch.stack(jacobian(top_F, purtabed_img, k_value + 1), dim=1)
gradients_max = torch.stack(jacobian(max_F, purtabed_img, 1), dim=1)
with torch.no_grad():
for idx in range(inputs.size(0)):
for k in range(k_value + 1):
if torch.all(torch.eq(gradients_top[idx, k, ...], gradients_max[idx,0,...]))==False and p[0][k]!=max_label:
norm = torch.div(1, torch.norm(gradients_top[idx, k, ...] - gradients_max[idx,0,...]))
w = w + (gradients_top[idx, k, ...] - gradients_max[idx,0,...]) * norm
f = f + (F[idx, p[0][k]] - F[idx, max_label]) * norm
r_tot[idx, ...] = r_tot[idx, ...] + torch.abs(f) * w / torch.norm(w)
purtabed_img = (torch.tanh(r_tot + timg) * boxmul + boxplus).requires_grad_()
F = model(purtabed_img)
p = torch.argsort(F, dim=1, descending=True)
purtabed_final_out = F.cpu().detach().numpy()
_,predict_label_final=nontargeted_TP_index(tlab.cpu().detach().numpy(), F.cpu().detach().numpy(), k_value)
purtabed_img_out = np.arctanh(((purtabed_img - boxplus) / boxmul * 0.999999).cpu().detach().numpy())
modifier_out = purtabed_img_out - timg.cpu().detach().numpy()
complement_set = all_list - set((p[0][:k_value]).cpu().detach().numpy())
if complement_set.issuperset(set(tlab_all)):
Flag, predict_label = nontargeted_TP_index(tlab.cpu().detach().numpy(), F.cpu().detach().numpy(), k_value)
purtabed_img_out = np.arctanh(((purtabed_img - boxplus) / boxmul * 0.999999).cpu().detach().numpy())
modifier_out = purtabed_img_out - timg.cpu().detach().numpy()
attack_success = True
c_list[0], c_list[1], c_list[2], c_list[3], c_list[4], c_list[5] = \
topk_acc_metric_1_to_10(GT.cpu().detach().numpy(), F.cpu().detach().numpy())
print('iter:', loop_i + 1, \
'attacked: ', attack_success, \
'predict_label:', predict_label, \
'GT:', tlab.cpu().detach().numpy(), \
'min:', "{:.5f}".format(r_tot.min().cpu()), \
'max:', "{:.5f}".format(r_tot.max().cpu()), \
'norm:', "{:.5f}".format(np.linalg.norm(r_tot.cpu().detach().numpy())))
loop_i = loop_i + 1
return purtabed_img_out, modifier_out, attack_success, c_list,purtabed_final_out, predict_label_final
def kUAP(args, model, device, val_loader, sample_list, train_index_end):
global_delta = torch.zeros(iter(val_loader).next()[0].shape).to(device)
fooling_rate = 0
itr = 0
boxmul = (args.boxmax - args.boxmin) / 2.
boxplus = (args.boxmin + args.boxmax) / 2.
while fooling_rate < args.ufr_lower_bound and itr < args.max_iter_uni:
print('Starting pass number ', itr)
index1 =0
for ith, (data, GT) in tqdm(enumerate(val_loader)):
if ith in sample_list[:train_index_end]:
if len(GT.shape) == 3:
GT = GT.max(dim=1)[0]
else:
pass
print('ith:', index1 + 1)
GT = GT.int()
print('\n')
data, GT = data.to(device), GT.to(device)
pred = model(torch.tanh(data + global_delta) * boxmul + boxplus)
Flag = bool(topk_acc_metric(GT.cpu().numpy(), pred.cpu().detach().numpy(), args.k_value))
if Flag == False:
purtabed_img_out, modifiered, attack_success, c_list = \
kfool(model, data, GT, args.k_value, args.boxmax, args.boxmin, args.maxiter, device=device,
lr=args.lr_attack)
if attack_success == True:
global_delta = global_delta + torch.from_numpy(modifiered).to(device)
print('pre_global_delta:', np.linalg.norm(global_delta.cpu().detach().numpy()))
global_delta = clip_eta(global_delta, args.uap_norm, args.uap_eps)
print('after_global_delta:', np.linalg.norm(global_delta.cpu().detach().numpy()))
else:
print('attack had successed')
index1 = index1 + 1
if index1 == train_index_end:
break
itr = itr + 1
count = 0
data_num = 0
index2 = 0
for ith, (data, GT) in tqdm(enumerate(val_loader)):
if ith in sample_list[:train_index_end]:
if len(GT.shape) == 3:
GT = GT.max(dim=1)[0]
else:
pass
data, GT = data.to(device), GT.to(device)
data_num = data_num + 1
pred = model(torch.tanh(data + global_delta) * boxmul + boxplus)
count = count + topk_acc_metric(GT.cpu().numpy(), pred.cpu().detach().numpy(), args.k_value)
index2 = index2 + 1
if index2 == train_index_end:
break
fooling_rate = count / data_num
print('FOOLING RATE = ', fooling_rate)
return global_delta
def save_result(success_c_list, index, args, success_img_index, success_modifier_norm_list, success_modifier_norm_index_0, success_modifier_norm_index_1, \
success_modifier_norm_index_2, success_modifier_norm_index_3,success_modifier_norm_index_4, success_modifier_norm_index_5):
final_all = []
c_list_sum = np.sum(np.asarray(success_c_list), 0) / index
if success_c_list != []:
print('attack_type= ', "{}".format(args.app), 'label_difficult= ', "{}".format(args.label_difficult))
print('FR_1= ', "{:.5f}".format(c_list_sum[0]), \
'FR_2= ', "{:.5f}".format(c_list_sum[1]), \
'FR_3= ', "{:.5f}".format(c_list_sum[2]), \
'FR_4= ', "{:.5f}".format(c_list_sum[3]), \
'FR_5= ', "{:.5f}".format(c_list_sum[4]), \
'FR_10= ', "{:.5f}".format(c_list_sum[5]))
print('avg_norm_1= ', "{}".format(
np.average(np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_0))))), \
'avg_norm_2= ', "{}".format(np.average(
np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_1))))), \
'avg_norm_3= ', "{}".format(np.average(
np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_2))))), \
'avg_norm_4= ', "{}".format(np.average(
np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_3))))), \
'avg_norm_5= ', "{}".format(np.average(
np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_4))))), \
'avg_norm_10= ', "{}".format(np.average(
np.asarray(list(map(success_modifier_norm_list.__getitem__, success_modifier_norm_index_5))))))
else:
print('All False')
# final_all.append(success_perturbated_img_list)
# final_all.append(success_modifier_list)
# final_all.append(success_modifier_norm_list)
final_all.append(success_img_index)
final_all.append(success_c_list)
final_all.append(success_modifier_norm_index_0)
final_all.append(success_modifier_norm_index_1)
final_all.append(success_modifier_norm_index_2)
final_all.append(success_modifier_norm_index_3)
final_all.append(success_modifier_norm_index_4)
final_all.append(success_modifier_norm_index_5)
os.makedirs('./result/{}/{}/eps_{}'.format(args.label_difficult, args.app, args.eps), exist_ok=True)
final_all = np.asarray(final_all, dtype=object)
np.save('./result/{}/{}/eps_{}/k_{}'.format(args.label_difficult, args.app, args.eps, args.k_value), final_all)
def baselineap(args, model, device, val_loader):
print('kvalue: ',args.k_value, 'label_difficult',args.label_difficult, 'app_type:', args.app)
model.eval()
success_count = 0
index = 0
index_success = 0
# success_perturbated_img_list = []
# success_modifier_list = []
success_modifier_norm_list = []
success_img_index = []
success_c_list = []
success_modifier_norm_index_0 = []
success_modifier_norm_index_1 = []
success_modifier_norm_index_2 = []
success_modifier_norm_index_3 = []
success_modifier_norm_index_4 = []
success_modifier_norm_index_5 = []
a = {}
if args.app == 'baseline_kfool':
a['best_3'] = [5]#874, 49
# a['best_3'] = [521] # 874, 49
a['best_5'] = [5]
a['best_10'] = [5]
elif args.app == 'baseline_rank':
if args.eps == 10:
a['best_3'] = [1118, 488, 460]
a['best_5'] = [310, 316, 814]
a['best_10'] = [858, 896, 316]
a['random_3'] = [309, 860, 828]
a['random_5'] = [721, 390, 603]
a['random_10'] = [1067, 978, 896]
a['worst_3'] = [858, 859, 721]
a['worst_5'] = [942, 870, 978]
a['worst_10'] = [1137, 521, 858]
elif args.eps == 2:
a['best_3'] = [277] # [277,313,412]
a['best_5'] = [277] # [277,313,412]
a['best_10'] = [277] # [277,313,412]
a['random_3'] = [247] # [247,9, 194]
a['random_5'] = [247] # [247, 874 ,746]
a['random_10'] = [247] # [247, 386, 720]
# a['random_3'] = [521] # [247,9, 194]
# a['random_5'] = [521] # [247, 874 ,746]
# a['random_10'] = [521] # [247, 386, 720]
a['worst_3'] = [647] # [614, 647, 1177]
a['worst_5'] = [647] # [614, 647, 1177]
a['worst_10'] = [647] # [614, 647, 1177]
elif args.app == 'baseline_kUAP':
a['best_3'] = [6574, 7138, 8114, 8184]
sample_list = np.load('ap_{}_list.npy'.format(args.dataset))
if args.app == 'baseline_kUAP':
boxmul = (args.boxmax - args.boxmin) / 2.
boxplus = (args.boxmin + args.boxmax) / 2.
for ith, (data, GT) in tqdm(enumerate(val_loader)):
if ith in a['{}_{}'.format(args.label_difficult, args.k_value)]:
if len(GT.shape) == 3:
GT = GT.max(dim=1)[0]
else:
pass
print('ith2:', index + 1)
data, GT = data.to(device), GT.to(device)
plot_list = []
plot_list.append(data.cpu().detach().numpy())
plot_list.append(GT.cpu().detach().numpy())
plot_list.append(np.argsort(-(model(data).cpu().detach().numpy())[0]))
global_delta = torch.from_numpy(np.load(
'./result/{}/{}/{}/eps_{}/perturbation_{}.npy'.format(args.dataset, args.label_difficult, args.app,
args.eps, args.k_value))).to(device)
plot_list.append((data + global_delta).cpu().detach().numpy())
pred = model(torch.tanh(data + global_delta) * boxmul + boxplus)
Flag, predict_label = nontargeted_TP_index(GT.cpu().numpy(), pred.cpu().detach().numpy(), args.k_value)
plot_list.append(predict_label)
os.makedirs(
'./plot_result/{}/{}/{}/eps_{}'.format(args.dataset, args.label_difficult, args.app, args.eps),
exist_ok=True)
plot_list = np.asarray(plot_list, dtype=object)
np.save(
'./plot_result/{}/{}/{}/eps_{}/images_result_k_{}_ith_{}'.format(args.dataset,
args.label_difficult, args.app,
args.eps,
args.k_value, ith), plot_list)
# pred = model(torch.tanh(data + global_delta) * boxmul + boxplus)
# Flag, predict_label = nontargeted_TP_index(GT.cpu().numpy(), pred.cpu().detach().numpy(), args.k_value)
# Flag = bool(topk_acc_metric(GT.cpu().numpy(), pred.cpu().detach().numpy(), args.k_value))
if Flag == True:
success_count = success_count + 1
c_list = [0] * 6
c_list[0], c_list[1], c_list[2], c_list[3], c_list[4], c_list[5] = \
topk_acc_metric_1_to_10(GT.cpu().detach().numpy(), pred.cpu().detach().numpy())
success_modifier_norm_list.append(np.linalg.norm(global_delta.cpu().detach().numpy())/((args.image_size)*(args.image_size)))
success_img_index.append(ith)
success_c_list.append(c_list)
if c_list[0] == 1:
success_modifier_norm_index_0.append(index_success)
if c_list[1] == 1:
success_modifier_norm_index_1.append(index_success)
if c_list[2] == 1:
success_modifier_norm_index_2.append(index_success)
if c_list[3] == 1:
success_modifier_norm_index_3.append(index_success)
if c_list[4] == 1:
success_modifier_norm_index_4.append(index_success)
if c_list[5] == 1:
success_modifier_norm_index_5.append(index_success)
index_success = index_success + 1
print('success:{}/{}'.format(success_count, index + 1))
index = index + 1
if index == (args.uap_test_index_end - args.uap_test_index_start):
break
if args.app == 'baseline_kfool':
for ith, (data, GT) in tqdm(enumerate(val_loader)):
if ith in a['{}_{}'.format(args.label_difficult, args.k_value)]:
if len(GT.shape) == 3:
GT = GT.max(dim=1)[0]
else:
pass
GT = GT.int()
if index < 1:
print('\n')
data, GT = data.to(device), GT.to(device)
plot_list = []
plot_list.append(data.cpu().detach().numpy())
plot_list.append(GT.cpu().detach().numpy())
plot_list.append(np.argsort(-(model(data).cpu().detach().numpy())[0]))
purtabed_img_out, modifier_out, attack_success, c_list, purtabed_final_out, predict_label =\
kfool(model, data, GT, args.k_value, args.boxmax, args.boxmin, args.maxiter, device=device, lr=args.lr_attack)
plot_list.append(purtabed_img_out)
plot_list.append(purtabed_final_out)
plot_list.append(predict_label)
plot_list.append(modifier_out)
os.makedirs(
'./plot_result/{}/{}/{}/eps_{}'.format(args.dataset, args.label_difficult, args.app, args.eps),
exist_ok=True)
plot_list = np.asarray(plot_list, dtype=object)
np.save(
'./plot_result/{}/{}/{}/eps_{}/images_result_k_{}_ith_{}'.format(args.dataset,
args.label_difficult, args.app,
args.eps,
args.k_value, ith), plot_list)
if attack_success:
success_count = success_count + 1
# success_perturbated_img_list.append(purtabed_img_out)
# success_modifier_list.append(modifier_out)
success_modifier_norm_list.append(np.linalg.norm(modifier_out)/((args.image_size)*(args.image_size)))
success_img_index.append(ith)
success_c_list.append(c_list)
if c_list[0] == 1:
success_modifier_norm_index_0.append(index_success)
if c_list[1] == 1:
success_modifier_norm_index_1.append(index_success)
if c_list[2] == 1:
success_modifier_norm_index_2.append(index_success)
if c_list[3] == 1:
success_modifier_norm_index_3.append(index_success)
if c_list[4] == 1:
success_modifier_norm_index_4.append(index_success)
if c_list[5] == 1:
success_modifier_norm_index_5.append(index_success)
index_success = index_success + 1
print('success:{}/{}'.format(success_count, index + 1))
index = index + 1
if index == 1:
break
if args.app == 'baseline_rank':
for ith, (data, GT) in tqdm(enumerate(val_loader)):
if ith in a['{}_{}'.format(args.label_difficult, args.k_value)]:
if len(GT.shape) == 3:
GT = GT.max(dim=1)[0]
else:
pass
print('ith:', ith)
GT = GT.int()
if index < 1:
print('\n')
data = data.to(device)
target, targets_zeros = generate_target_zeros_3_cases(model, data, GT.cpu().detach().numpy(),
args.label_difficult, k=args.k_value)
plot_list = []
plot_list.append(data.cpu().detach().numpy())
plot_list.append(GT.cpu().detach().numpy())
plot_list.append(np.argsort(-(model(data).cpu().detach().numpy())[0]))
plot_list.append(targets_zeros)
targets_zeros = torch.from_numpy(targets_zeros).to(device)
######MLAP rank I baseline from the paper 'Multi-Label Adversarial Perturbations'
purtabed_img_out, modifier_out, attack_success, c_list, purtabed_final_out, predict_label = \
rank2(model, data, targets_zeros, args.k_value, args.eps, args.maxiter, args.boxmax, args.boxmin, \
GT, device=device, lr=args.lr_attack)
plot_list.append(purtabed_img_out)
plot_list.append(purtabed_final_out)
plot_list.append(predict_label)
plot_list.append(modifier_out)
os.makedirs('./plot_result/{}/{}/{}/eps_{}'.format(args.dataset, args.label_difficult, args.app, args.eps),
exist_ok=True)
plot_list = np.asarray(plot_list, dtype=object)
np.save(
'./plot_result/{}/{}/{}/eps_{}/images_result_k_{}_ith_{}'.format(args.dataset, args.label_difficult, args.app, args.eps,
args.k_value, ith),
plot_list)
if attack_success:
success_count = success_count + 1
# success_perturbated_img_list.append(purtabed_img_out)
# success_modifier_list.append(modifier_out)
success_modifier_norm_list.append(np.linalg.norm(modifier_out)/((args.image_size)*(args.image_size)))
success_img_index.append(ith)
success_c_list.append(c_list)
if c_list[0] == 1:
success_modifier_norm_index_0.append(index_success)
if c_list[1] == 1:
success_modifier_norm_index_1.append(index_success)
if c_list[2] == 1:
success_modifier_norm_index_2.append(index_success)
if c_list[3] == 1:
success_modifier_norm_index_3.append(index_success)
if c_list[4] == 1:
success_modifier_norm_index_4.append(index_success)
if c_list[5] == 1:
success_modifier_norm_index_5.append(index_success)
index_success = index_success + 1
print('success:{}/{}'.format(success_count, index + 1))
index = index + 1
if index == 1:
break
# save_result(success_c_list, index, args, success_img_index, success_modifier_norm_list,
# success_modifier_norm_index_0, success_modifier_norm_index_1, \
# success_modifier_norm_index_2, success_modifier_norm_index_3, \
# success_modifier_norm_index_4, success_modifier_norm_index_5)
torch.cuda.empty_cache()