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mora_attack.py
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mora_attack.py
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import numpy as np
import time
import torch
import os
import sys
import math
import torch.nn as nn
import torch.nn.functional as F
class MORAAttack():
def __init__(self, model, n_iter=100, norm='Linf', n_restarts=1, eps=None,
seed=0, loss='ce', eot_iter=1, rho=.75, verbose=False,
device='cuda', decay_step='linear', float_dis=1.0, version='pgd', ensemble_pattern='softmax'):
self.model = model
self.n_iter = n_iter
self.eps = eps - 1.9 * 1e-8
self.norm = norm
self.n_restarts = n_restarts
self.seed = seed
self.loss = loss
self.eot_iter = eot_iter
self.thr_decr = rho
self.verbose = verbose
self.device = device
self.scale = True
self.decay_step = decay_step
self.float_dis = float_dis
self.version = version
self.ensemble_pattern = ensemble_pattern
def dlr_loss(self, x, y):
x_sorted, ind_sorted = x.sort(dim=1)
ind = (ind_sorted[:, -1] == y).float()
return -(x[np.arange(x.shape[0]), y] - x_sorted[:, -2] * ind - x_sorted[:, -1] * (1. - ind)) / (
x_sorted[:, -1] - x_sorted[:, -3] + 1e-12)
def cw_loss(self, x, y):
x_sorted, ind_sorted = x.sort(dim=1)
ind = (ind_sorted[:, -1] == y).float()
return -(x[np.arange(x.shape[0]), y] - x_sorted[:, -2] * ind - x_sorted[:, -1] * (1. - ind))
def check_right_index(self, output, labels):
output_index = output.argmax(dim=-1) == labels
mask = output_index.to(dtype=torch.int8)
mask = torch.unsqueeze(mask, -1)
return mask
def get_output_scale(self, output):
std_max_out = []
maxk = max((10,))
# topk from big to small
pred_val_out, pred_id_out = output.topk(maxk, 1, True, True)
std_max_out.extend((pred_val_out[:, 0] - pred_val_out[:, 1]).cpu().numpy())
scale_list = [item / 1.0 for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_sub_max_scale(self, outputs, len_output, y):
std_max_out = [0 for i in range(len_output)]
for i in range(len_output):
output_i = outputs[i].clone().detach()
output_i_sorted, ind_i_sorted = output_i.sort(dim=1)
ind_i = (ind_i_sorted[:, -1] == y).float()
std_max_out[i] = torch.abs(output_i_sorted[:, -1] - output_i_sorted[:, -2]).cpu().numpy()
max_out = std_max_out[0]
for j in range(len_output - 1):
for k in range(len(std_max_out[j + 1])):
if max_out[k] < std_max_out[j + 1][k]:
max_out[k] = std_max_out[j + 1][k]
scale_list = [np.abs(item) / 10.0 if item > 10.0 else 1.0 for item in max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_weight(self, x, y):
std_max_out = []
# x.sort from small to big
x_sorted, ind_sorted = x.sort(dim=1)
ind = (ind_sorted[:, -1] == y).float()
std_max_out.extend(
(x[np.arange(x.shape[0]), y] - x_sorted[:, -2] * ind - x_sorted[:, -1] * (1. - ind)).cpu().numpy())
scale_list = [math.exp(self.float_dis * np.abs(item)) / math.pow(1 + math.exp(self.float_dis * np.abs(item)), 2)
if self.float_dis * np.abs(item) < 100 else 1e-8 for item in std_max_out]
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_sub_weight(self, x_ensemble, x_sub, y):
std_max_out = []
x_ensemble_sorted, ind_ensemble_sorted = x_ensemble.sort(dim=1)
ind = (ind_ensemble_sorted[:, -1] == y).float()
std_max_out.extend((x_sub[np.arange(x_sub.shape[0]), y] -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -1]] * (1. - ind) -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -2]] * ind).cpu().numpy())
scale_list = [
math.exp(self.float_dis * np.abs(item)) / math.pow(1 + math.exp(self.float_dis * np.abs(item)), 2) if
self.float_dis * np.abs(item) < 10.0 else 1.0 / np.abs(item) for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_probablity_weight(self, x_ensemble, x_sub, y):
std_max_out = []
x_ensemble_sorted, ind_ensemble_sorted = x_ensemble.sort(dim=1)
ind = (ind_ensemble_sorted[:, -1] == y).float()
p_z_max = (x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -1]] * (1. - ind) -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -2]] * ind)
p_y = x_sub[np.arange(x_sub.shape[0]), y]
z_y = x_ensemble[np.arange(x_ensemble.shape[0]), y]
z_max = (x_ensemble[np.arange(x_ensemble.shape[0]), ind_ensemble_sorted[:, -1]] * (1. - ind) -
x_ensemble[np.arange(x_ensemble.shape[0]), ind_ensemble_sorted[:, -2]] * ind)
std_max_out.extend( (p_z_max*(p_y/z_y + (1-p_z_max)/z_max )).cpu().numpy() )
scale_list = [item for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def attack_single_run(self, x_in, y_in):
x_begin_batch = x_in.clone() if len(x_in.shape) == 4 else x_in.clone().unsqueeze(0)
y_begin_batch = y_in.clone() if len(y_in.shape) == 1 else y_in.clone().unsqueeze(0)
if self.norm == 'Linf':
t = 2 * torch.rand(x_begin_batch.shape).to(self.device).detach() - 1
x_adv_begin_batch = x_begin_batch.detach() + self.eps * torch.ones(
[x_begin_batch.shape[0], 1, 1, 1]).to(self.device).detach() * t / (
t.reshape([t.shape[0], -1]).abs().max(dim=1, keepdim=True)[0].reshape(
[-1, 1, 1, 1]))
elif self.norm == 'L2':
t = torch.randn(x_begin_batch.shape).to(self.device).detach()
x_adv_begin_batch = x_begin_batch.detach() + self.eps * torch.ones([x_begin_batch.shape[0], 1, 1, 1]).to(
self.device).detach() * t / ((t ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12)
x_adv_begin_batch = x_adv_begin_batch.clamp(0., 1.)
x_best_adv = x_adv_begin_batch.clone()
if self.loss == 'ce':
criterion_indiv = nn.CrossEntropyLoss(reduce=False, reduction='none')
elif self.loss == 'dlr':
criterion_indiv = self.dlr_loss
elif self.loss == 'cw':
criterion_indiv = self.cw_loss
else:
raise ValueError('unknowkn loss')
x_adv_begin_batch.requires_grad_()
grad = torch.zeros_like(x_begin_batch)
logits = self.model(x_adv_begin_batch)[-1]
acc_batch = logits.max(1)[1] == y_begin_batch
robust_flags_batch = acc_batch
num_robust_batch = torch.sum(robust_flags_batch).item()
robust_lin_indcs_batch = robust_flags_batch.nonzero().squeeze(dim=1)
x_adv = x_adv_begin_batch[robust_lin_indcs_batch, :]
x = x_begin_batch[robust_lin_indcs_batch, :]
y = y_begin_batch[robust_lin_indcs_batch]
logits = logits[robust_lin_indcs_batch, :]
step_size_begin = 2 * self.eps
x_adv_old = x_adv.clone()
adv_acc_result_batch = torch.zeros(self.n_iter, dtype=torch.float32, device=self.device)
for i in range(self.n_iter):
if self.decay_step == 'linear':
step_size = step_size_begin * (1 - i / self.n_iter)
elif self.decay_step == 'cosine':
step_size = step_size_begin * (1 + math.cos(i / self.n_iter * math.pi)) * 0.5
elif self.decay_step == 'cos':
step_size = step_size_begin * math.cos(i / self.n_iter * math.pi * 0.5)
elif self.decay_step == 'constant':
step_size = torch.ones([x.shape[0], 1, 1, 1]).to(self.device).detach() * torch.Tensor([2.0 / 255.0]).to(
self.device).detach().reshape([1, 1, 1, 1])
x_adv.requires_grad_()
grad = torch.zeros_like(x)
for _ in range(self.eot_iter):
with torch.enable_grad():
x_adv_input = x_adv
outputs = self.model(x_adv_input) # 1 forward pass (eot_iter = 1)
len_output = len(outputs)
mask_out_sub_original = [0 for i_sub in range(len_output)]
scale_out_sub_original = [0 for i_sub in range(len_output)]
weight_out_sub_original = [0 for i_sub in range(len_output)]
weight_out_sub_probility = [0 for i_sub in range(len_output)]
out_adv_scale, out_adv_old = outputs[-2], outputs[-1]
mask_out_adv = self.check_right_index(out_adv_old, y)
mask_out_adv_grad = torch.unsqueeze(torch.unsqueeze(mask_out_adv.clone(), -1), -1) # #############
scale_output_old = self.get_output_scale(out_adv_scale.clone().detach())
sub_output = len_output - 1
sub_max_out_scale = self.get_sub_max_scale(outputs, sub_output, y)
scale_sub_output = [0 for i_sub in range(sub_output)]
outputs_after_scale = 0
for i_sub in range(sub_output):
scale_sub_output[i_sub] = outputs[i_sub] / sub_max_out_scale
for i_sub in range(sub_output - 1):
outputs_after_scale += F.softmax(scale_sub_output[i_sub].clone().detach(), dim=-1)
outputs_after_scale = outputs_after_scale / sub_output
for i_pre in range(sub_output):
scale_out_sub_original[i_pre] = self.get_output_scale(outputs[i_pre].clone().detach())
mask_out_sub_original[i_pre] = self.check_right_index(scale_sub_output[i_pre], y)
weight_out_sub_original[i_pre] = self.get_sub_weight(out_adv_scale.clone().detach(),
scale_sub_output[i_pre].clone().detach(),
y)
sub_after_softmax = [0 for i_sub in range(sub_output)]
for i_pre in range(sub_output):
sub_after_softmax[i_pre] = F.softmax(self.float_dis * outputs[i_pre], dim=-1)
weight_out_sub_probility[i_pre] = self.get_probablity_weight(
outputs_after_scale.clone().detach(), sub_after_softmax[i_pre].clone().detach(), y)
scale = self.scale_value
if self.ensemble_pattern == 'softmax' or self.ensemble_pattern=='voting':
logits_prev = (1 - scale) * out_adv_scale / scale_output_old
loss_sub = 0
weight_sub = 1e-8
for i_pre in range(sub_output):
loss_sub += scale_sub_output[i_pre] * weight_out_sub_probility[i_pre] * \
mask_out_sub_original[i_pre]
weight_sub += weight_out_sub_probility[i_pre] * mask_out_sub_original[i_pre]
logits_prev += scale * (loss_sub / weight_sub)
elif self.ensemble_pattern == 'logits':
logits_prev = (1 - scale) * out_adv_scale / scale_output_old
loss_sub = 0
weight_sub = 1e-8
for i_pre in range(sub_output):
loss_sub += scale_sub_output[i_pre] * weight_out_sub_probility[i_pre]
weight_sub += weight_out_sub_probility[i_pre]
logits_prev += scale * (loss_sub / weight_sub)
loss_indiv_prev = criterion_indiv(logits_prev, y)
loss_prev = loss_indiv_prev.sum()
logits = out_adv_old
grad += torch.autograd.grad(loss_prev, [x_adv])[0].detach()
grad /= float(self.eot_iter)
with torch.no_grad():
x_adv = x_adv.detach()
grad2 = x_adv - x_adv_old
x_adv_old = x_adv.clone()
a = 0.75 if i > 0 else 1.0
if self.norm == 'Linf':
if self.version == 'standard-t-pre' or self.version == 'adaptive_attack':
x_adv_1 = x_adv + mask_out_adv_grad * step_size * torch.sign(grad)
x_adv_1 = torch.clamp(torch.min(torch.max(x_adv_1, x - self.eps), x + self.eps), 0.0, 1.0)
x_adv_1 = torch.clamp(torch.min(
torch.max(x_adv + mask_out_adv_grad * ((x_adv_1 - x_adv) * a + grad2 * (1 - a)),
x - self.eps),
x + self.eps), 0.0, 1.0)
else:
x_adv_1 = x_adv + step_size * torch.sign(grad)
x_adv_1 = torch.clamp(torch.min(torch.max(x_adv_1, x - self.eps), x + self.eps), 0.0, 1.0)
x_adv_1 = torch.clamp(torch.min(
torch.max(x_adv + ((x_adv_1 - x_adv) * a + grad2 * (1 - a)),
x - self.eps),
x + self.eps), 0.0, 1.0)
elif self.norm == 'L2':
x_adv_1 = x_adv + step_size * grad / ((grad ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12)
x_adv_1 = torch.clamp(x + (x_adv_1 - x) / (
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12) * torch.min(
self.eps * torch.ones(x.shape).to(self.device).detach(),
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt()), 0.0, 1.0)
x_adv_1 = x_adv + (x_adv_1 - x_adv) * a + grad2 * (1 - a)
x_adv_1 = torch.clamp(x + (x_adv_1 - x) / (
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12) * torch.min(
self.eps * torch.ones(x.shape).to(self.device).detach(),
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12), 0.0, 1.0)
x_adv = x_adv_1 + 0.
logits_after_attack = self.model(x_adv)[-1]
pred = logits_after_attack.detach().max(1)[1] == y
false_batch = ~ pred
right_batch = pred
non_robust_lin_idcs = robust_lin_indcs_batch[false_batch]
robust_flags_batch[non_robust_lin_idcs] = False
acc_batch[non_robust_lin_idcs] = False
x_best_adv[non_robust_lin_idcs] = x_adv[false_batch]
robust_lin_indcs_batch = robust_flags_batch.nonzero().squeeze(dim=1)
x_adv_middle = x_adv[right_batch, :]
x_adv = x_adv_middle
x_adv_old = x_adv_old[right_batch, :]
grad = grad[right_batch, :]
x = x_begin_batch[robust_lin_indcs_batch, :]
y = y_begin_batch[robust_lin_indcs_batch]
num_robust_batch = torch.sum(robust_flags_batch).item()
if num_robust_batch == 0:
break
adv_acc_result_batch[i] += num_robust_batch
return acc_batch, x_best_adv, adv_acc_result_batch
def perturb(self, x_in, y_in, best_loss=False, cheap=True, scale=0):
self.scale_value = scale
assert self.norm in ['Linf', 'L2']
x = x_in.clone().unsqueeze(0) if len(x_in.shape) == 3 else x_in.clone()
y = y_in.clone().unsqueeze(0) if len(y_in.shape) == 0 else y_in.clone()
adv = x.clone()
x_input = x
acc = self.model(x_input)[-1].max(1)[1] == y
if self.verbose:
print('-------------------------- running {}-attack with epsilon {:.4f} --------------------------'.format(
self.norm, self.eps))
print('initial accuracy: {:.2%}'.format(acc.float().mean()))
startt = time.time()
torch.random.manual_seed(self.seed)
torch.cuda.random.manual_seed(self.seed)
adv_acc_result_batch = torch.zeros(self.n_iter * self.n_restarts, dtype=torch.float32, device=self.device)
ind_to_fool = acc.nonzero().squeeze()
if len(ind_to_fool.shape) == 0: ind_to_fool = ind_to_fool.unsqueeze(0)
if ind_to_fool.numel() != 0:
x_to_fool, y_to_fool = x[ind_to_fool].clone(), y[ind_to_fool].clone()
acc_curr, adv_curr, adv_acc_result_batch_every_counter = self.attack_single_run(x_to_fool, y_to_fool)
ind_curr = (acc_curr == 0).nonzero().squeeze()
for i in range(len(adv_acc_result_batch_every_counter)):
adv_acc_result_batch[0 * self.n_iter + i] = adv_acc_result_batch_every_counter[i]
acc[ind_to_fool[ind_curr]] = 0
adv[ind_to_fool[ind_curr]] = adv_curr[ind_curr].clone()
if self.verbose:
print('restart {} - robust accuracy: {:.2%} - cum. time: {:.1f} s'.format(
counter, acc.float().mean(), time.time() - startt))
return acc, adv, adv_acc_result_batch
class MORAAttack_targeted():
def __init__(self, model, n_iter=100, norm='Linf', n_restarts=1, eps=None,
seed=0, loss='ce', eot_iter=1, rho=.75, verbose=False, device='cuda',
n_target_classes=9, decay_step='linear', float_dis=1.0, version='pgd', ensemble_pattern='softmax'):
self.model = model
self.n_iter = n_iter
self.eps = eps - 1.9 * 1e-8
self.norm = norm
self.n_restarts = n_restarts
self.seed = seed
self.eot_iter = eot_iter
self.thr_decr = rho
self.verbose = verbose
self.target_class = None
self.device = device
self.n_target_classes = n_target_classes
self.loss = loss
self.scale = True
self.decay_step = decay_step
self.float_dis = float_dis
self.version = version
self.ensemble_pattern = ensemble_pattern
def check_right_index(self, output, labels):
output_index = output.argmax(dim=-1) == labels
mask = output_index.to(dtype=torch.int8)
mask = torch.unsqueeze(mask, -1)
return mask
def dlr_loss_targeted(self, x, y, y_target):
x_sorted, ind_sorted = x.sort(dim=1)
return -(x[np.arange(x.shape[0]), y] - x[np.arange(x.shape[0]), y_target]) / (
x_sorted[:, -1] - .5 * x_sorted[:, -3] - .5 * x_sorted[:, -4] + 1e-12)
def ce_targeted(self, x, y, y_target):
criterion = nn.CrossEntropyLoss(reduce=False, reduction='none')
return -criterion(x, y_target)
def get_output_scale(self, output):
std_max_out = []
maxk = max((10,))
# topk from big to small
pred_val_out, pred_id_out = output.topk(maxk, 1, True, True)
std_max_out.extend((pred_val_out[:, 0] - pred_val_out[:, 1]).cpu().numpy())
scale_list = [np.abs(item) / 1.0 for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_sub_max_scale(self, outputs, len_output, y):
std_max_out = [0 for i in range(len_output)]
for i in range(len_output):
output_i = outputs[i].clone().detach()
output_i_sorted, ind_i_sorted = output_i.sort(dim=1)
ind_i = (ind_i_sorted[:, -1] == y).float()
std_max_out[i] = torch.abs(output_i_sorted[:, -1] - output_i_sorted[:, -2]).cpu().numpy()
max_out = std_max_out[0]
for j in range(len_output - 1):
for k in range(len(std_max_out[j + 1])):
if max_out[k] < std_max_out[j + 1][k]:
max_out[k] = std_max_out[j + 1][k]
scale_list = [np.abs(item) / 10.0 if item > 10.0 else 1.0 for item in max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_weight(self, x, y):
std_max_out = []
x_sorted, ind_sorted = x.sort(dim=1)
ind = (ind_sorted[:, -1] == y).float()
std_max_out.extend(
(x[np.arange(x.shape[0]), y] - x_sorted[:, -2] * ind - x_sorted[:, -1] * (1. - ind)).cpu().numpy())
scale_list = [
math.exp(self.float_dis * np.abs(item)) / math.pow(1 + math.exp(self.float_dis * np.abs(item)), 2) if
self.float_dis * np.abs(item) < 10.0 else 1.0 / np.abs(item) for item in std_max_out]
# scale_list = [1.0 for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_sub_weight(self, x_ensemble, x_sub, y):
std_max_out = []
x_ensemble_sorted, ind_ensemble_sorted = x_ensemble.sort(dim=1)
ind = (ind_ensemble_sorted[:, -1] == y).float()
std_max_out.extend((x_sub[np.arange(x_sub.shape[0]), y] -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -1]] * (1. - ind) -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -2]] * ind).cpu().numpy())
scale_list = [1.0 / math.exp(self.float_dis * item) if self.float_dis * np.abs(item) < 100 else 1e-40 for item
in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def get_probablity_weight(self, x_ensemble, x_sub, y, y_target):
std_max_out = []
x_ensemble_sorted, ind_ensemble_sorted = x_ensemble.sort(dim=1)
ind = (ind_ensemble_sorted[:, -1] == y).float()
p_z_max = (x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -1]] * (1. - ind) -
x_sub[np.arange(x_sub.shape[0]), ind_ensemble_sorted[:, -2]] * ind)
p_y = x_sub[np.arange(x_sub.shape[0]), y]
std_max_out.extend((p_z_max * (p_y + 1 - p_z_max)).cpu().numpy())
scale_list = [item for item in std_max_out]
scale_list = torch.tensor(scale_list).to(self.device)
scale_list = torch.unsqueeze(scale_list, -1)
return scale_list
def attack_single_run(self, x_in, y_in):
x = x_in.clone().unsqueeze(0) if len(x_in.shape) == 3 else x_in.clone()
y = y_in.clone().unsqueeze(0) if len(y_in.shape) == 0 else y_in.clone()
if self.norm == 'Linf':
t = 2 * torch.rand(x.shape).to(self.device).detach() - 1
x_adv = x.detach() + self.eps * torch.ones([x.shape[0], 1, 1, 1]).to(self.device).detach() * t / (
t.reshape([t.shape[0], -1]).abs().max(dim=1, keepdim=True)[0].reshape([-1, 1, 1, 1]))
elif self.norm == 'L2':
t = torch.randn(x.shape).to(self.device).detach()
x_adv = x.detach() + self.eps * torch.ones([x.shape[0], 1, 1, 1]).to(self.device).detach() * t / (
(t ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12)
x_adv = torch.clamp(torch.min(torch.max(x_adv, x - self.eps), x + self.eps), 0.0, 1.0)
x_adv = x_adv.clamp(0., 1.)
x_best_adv = x_adv.clone()
x_input = x
output = self.model(x_input)
y_target = output[-1].sort(dim=1)[1][:, -self.target_class]
x_adv.requires_grad_()
grad = torch.zeros_like(x)
if self.loss == 'ce':
criterion_indiv = self.ce_targeted
elif self.loss == 'dlr':
criterion_indiv = self.dlr_loss_targeted
else:
raise ValueError('unknowkn loss')
acc = self.model(x)[-1].max(1)[1] == y
step_size_begin = self.eps * torch.ones([x.shape[0], 1, 1, 1]).to(self.device).detach() * torch.Tensor(
[2.0]).to(self.device).detach().reshape([1, 1, 1, 1])
x_adv_old = x_adv.clone()
for i in range(self.n_iter):
if self.decay_step == 'linear':
step_size = step_size_begin * (1 - i / self.n_iter)
elif self.decay_step == 'cosine':
step_size = step_size_begin * (1 + math.cos(i / self.n_iter * math.pi)) * 0.5
elif self.decay_step == 'cos':
step_size = step_size_begin * math.cos(i / self.n_iter * math.pi * 0.5)
elif self.decay_step == 'constant':
step_size = torch.ones([x.shape[0], 1, 1, 1]).to(self.device).detach() * torch.Tensor([2.0 / 255.0]).to(
self.device).detach().reshape([1, 1, 1, 1])
### get gradient
x_adv.requires_grad_()
grad = torch.zeros_like(x)
for _ in range(self.eot_iter):
with torch.enable_grad():
x_adv_input = x_adv
outputs = self.model(x_adv_input) # 1 forward pass (eot_iter = 1)
len_output = np.int(len(outputs))
sub_output = len_output - 1
mask_out_sub_original = [0 for i in range(len_output)]
scale_out_sub_original = [0 for i in range(len_output)]
weight_out_sub_original = [0 for i in range(len_output)]
weight_out_sub_probility = [0 for i in range(len_output)]
out_adv_scale, out_adv_old = outputs[-2], outputs[-1]
mask_out_adv = self.check_right_index(out_adv_old, y)
mask_out_adv_grad = torch.unsqueeze(torch.unsqueeze(mask_out_adv.clone(), -1), -1) # #############
scale_output_old = self.get_output_scale(out_adv_scale.clone().detach())
sub_max_out_scale = self.get_sub_max_scale(outputs, sub_output, y)
scale_sub_output = [0 for i in range(sub_output)]
outputs_after_scale = 0
for i in range(sub_output):
scale_sub_output[i] = outputs[i] / sub_max_out_scale
for i in range(sub_output - 1):
outputs_after_scale += F.softmax(scale_sub_output[i].clone().detach(), dim=-1)
outputs_after_scale = outputs_after_scale / sub_output
for i_pre in range(sub_output):
scale_out_sub_original[i_pre] = self.get_output_scale(outputs[i_pre].clone().detach())
mask_out_sub_original[i_pre] = self.check_right_index(scale_sub_output[i_pre], y)
weight_out_sub_original[i_pre] = self.get_sub_weight(out_adv_scale.clone().detach(),
scale_sub_output[i_pre].clone().detach(),
y)
# ##### get probability weight
sub_after_softmax = [0 for i in range(sub_output)]
for i_pre in range(sub_output):
sub_after_softmax[i_pre] = F.softmax(self.float_dis * outputs[i_pre], dim=-1)
weight_out_sub_probility[i_pre] = self.get_probablity_weight(
outputs_after_scale.clone().detach(), sub_after_softmax[i_pre].clone().detach(), y,
y_target)
scale = self.scale_value
if self.ensemble_pattern == 'softmax' or self.ensemble_pattern == 'voting':
logits_prev = (1 - scale) * out_adv_scale / scale_output_old
loss_sub = 0
weight_sub = 1e-8
for i_pre in range(sub_output):
loss_sub += scale_sub_output[i_pre] * weight_out_sub_probility[i_pre] * \
mask_out_sub_original[i_pre]
weight_sub += weight_out_sub_probility[i_pre] * mask_out_sub_original[i_pre]
logits_prev += scale * (loss_sub / weight_sub)
elif self.ensemble_pattern == 'logits':
logits_prev = (1 - scale) * out_adv_scale / scale_output_old
loss_sub = 0
weight_sub = 1e-8
for i_pre in range(sub_output):
loss_sub += scale_sub_output[i_pre] * weight_out_sub_probility[i_pre]
weight_sub += weight_out_sub_probility[i_pre]
logits_prev += scale * (loss_sub / weight_sub)
logits_prev = out_adv_old
loss_indiv_prev = criterion_indiv(logits_prev, y, y_target)
loss_prev = loss_indiv_prev.sum()
logits = out_adv_old
grad += torch.autograd.grad(loss_prev, [x_adv])[0].detach() # 1 backward pass (eot_iter = 1)
grad /= float(self.eot_iter)
with torch.no_grad():
x_adv = x_adv.detach()
grad2 = x_adv - x_adv_old
x_adv_old = x_adv.clone()
a = 0.75 if i > 0 else 1.0
if self.norm == 'Linf':
x_adv_1 = x_adv + mask_out_adv_grad * step_size * torch.sign(grad)
x_adv_1 = torch.clamp(torch.min(torch.max(x_adv_1, x - self.eps), x + self.eps), 0.0, 1.0)
x_adv_1 = torch.clamp(torch.min(
torch.max(x_adv + mask_out_adv_grad * ((x_adv_1 - x_adv) * a + grad2 * (1 - a)),
x - self.eps),
x + self.eps), 0.0, 1.0)
elif self.norm == 'L2':
x_adv_1 = x_adv + step_size[0] * grad / (
(grad ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12)
x_adv_1 = torch.clamp(x + (x_adv_1 - x) / (
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12) * torch.min(
self.eps * torch.ones(x.shape).to(self.device).detach(),
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt()), 0.0, 1.0)
x_adv_1 = x_adv + (x_adv_1 - x_adv) * a + grad2 * (1 - a)
x_adv_1 = torch.clamp(x + (x_adv_1 - x) / (
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12) * torch.min(
self.eps * torch.ones(x.shape).to(self.device).detach(),
((x_adv_1 - x) ** 2).sum(dim=(1, 2, 3), keepdim=True).sqrt() + 1e-12), 0.0, 1.0)
x_adv = x_adv_1 + 0.
logits_after_attack = self.model(x_adv)[-1]
pred = logits_after_attack.detach().max(1)[1] == y
acc = torch.min(acc, pred)
x_best_adv[(pred == 0).nonzero().squeeze()] = x_adv[(pred == 0).nonzero().squeeze()] + 0.
x_best_adv[(pred == 1).nonzero().squeeze()] = x_adv[(pred == 1).nonzero().squeeze()] + 0.
return acc, x_best_adv
def perturb(self, x_in, y_in, best_loss=False, cheap=True, scale=0):
self.scale_value = scale
assert self.norm in ['Linf', 'L2']
x = x_in.clone().unsqueeze(0) if len(x_in.shape) == 3 else x_in.clone()
y = y_in.clone().unsqueeze(0) if len(y_in.shape) == 0 else y_in.clone()
adv = x.clone()
x_input = x
acc = self.model(x_input)[-1].max(1)[1] == y
if self.verbose:
print('-------------------------- running {}-attack with epsilon {:.4f} --------------------------'.format(
self.norm, self.eps))
print('initial accuracy: {:.2%}'.format(acc.float().mean()))
startt = time.time()
torch.random.manual_seed(self.seed)
torch.cuda.random.manual_seed(self.seed)
for target_class in range(2, self.n_target_classes + 2):
self.target_class = target_class
ind_to_fool = acc.nonzero().squeeze()
if len(ind_to_fool.shape) == 0: ind_to_fool = ind_to_fool.unsqueeze(0)
if ind_to_fool.numel() != 0:
x_to_fool, y_to_fool = x[ind_to_fool].clone(), y[ind_to_fool].clone()
acc_curr, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
ind_curr = (acc_curr == 0).nonzero().squeeze()
#
acc[ind_to_fool[ind_curr]] = 0
adv[ind_to_fool[ind_curr]] = adv_curr[ind_curr].clone()
if self.verbose:
print(
'restart {} - target_class {} - robust accuracy: {:.2%} at eps = {:.5f} - cum. time: {:.1f} s'.format(
counter, self.target_class, acc.float().mean(), self.eps, time.time() - startt))
return acc, adv