/
attack_methods.py
executable file
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/
attack_methods.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models import *
from torch.autograd.gradcheck import zero_gradients
from torch.autograd import Variable
import utils
import math
from utils import softCrossEntropy
from utils import one_hot_tensor, label_smoothing
import ot
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class Attack_None(nn.Module):
def __init__(self, basic_net, config):
super(Attack_None, self).__init__()
self.train_flag = True if 'train' not in config.keys(
) else config['train']
self.basic_net = basic_net
print(config)
def forward(self, inputs, targets, attack=None, batch_idx=-1):
if self.train_flag:
self.basic_net.train()
else:
self.basic_net.eval()
outputs, _ = self.basic_net(inputs)
return outputs, None
class Attack_PGD(nn.Module):
# Back-propogate
def __init__(self, basic_net, config, attack_net=None):
super(Attack_PGD, self).__init__()
self.basic_net = basic_net
self.attack_net = attack_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.epsilon = config['epsilon']
self.num_steps = config['num_steps']
self.loss_func = torch.nn.CrossEntropyLoss(
reduction='none') if 'loss_func' not in config.keys(
) else config['loss_func']
self.train_flag = True if 'train' not in config.keys(
) else config['train']
self.box_type = 'white' if 'box_type' not in config.keys(
) else config['box_type']
print(config)
def forward(self,
inputs,
targets,
attack=True,
targeted_label=-1,
batch_idx=0):
if not attack:
outputs = self.basic_net(inputs)[0]
return outputs, None
if self.box_type == 'white':
aux_net = pickle.loads(pickle.dumps(self.basic_net))
elif self.box_type == 'black':
assert self.attack_net is not None, "should provide an additional net in black-box case"
aux_net = pickle.loads(pickle.dumps(self.basic_net))
aux_net.eval()
logits_pred_nat = aux_net(inputs)[0]
targets_prob = F.softmax(logits_pred_nat.float(), dim=1)
num_classes = targets_prob.size(1)
outputs = aux_net(inputs)[0]
targets_prob = F.softmax(outputs.float(), dim=1)
y_tensor_adv = targets
step_sign = 1.0
x = inputs.detach()
if self.rand:
x = x + torch.zeros_like(x).uniform_(-self.epsilon, self.epsilon)
x_org = x.detach()
loss_array = np.zeros((inputs.size(0), self.num_steps))
for i in range(self.num_steps):
x.requires_grad_()
zero_gradients(x)
if x.grad is not None:
x.grad.data.fill_(0)
aux_net.eval()
logits = aux_net(x)[0]
loss = self.loss_func(logits, y_tensor_adv)
loss = loss.mean()
aux_net.zero_grad()
loss.backward()
x_adv = x.data + step_sign * self.step_size * torch.sign(
x.grad.data)
x_adv = torch.min(torch.max(x_adv, inputs - self.epsilon),
inputs + self.epsilon)
x_adv = torch.clamp(x_adv, -1.0, 1.0)
x = Variable(x_adv)
if self.train_flag:
self.basic_net.train()
else:
self.basic_net.eval()
logits_pert = self.basic_net(x.detach())[0]
return logits_pert, targets_prob.detach()
class Attack_FeaScatter(nn.Module):
def __init__(self, basic_net, config, attack_net=None):
super(Attack_FeaScatter, self).__init__()
self.basic_net = basic_net
self.attack_net = attack_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.epsilon = config['epsilon']
self.num_steps = config['num_steps']
self.train_flag = True if 'train' not in config.keys(
) else config['train']
self.box_type = 'white' if 'box_type' not in config.keys(
) else config['box_type']
self.ls_factor = 0.1 if 'ls_factor' not in config.keys(
) else config['ls_factor']
print(config)
def forward(self,
inputs,
targets,
attack=True,
targeted_label=-1,
batch_idx=0):
if not attack:
outputs, _ = self.basic_net(inputs)
return outputs, None
if self.box_type == 'white':
aux_net = pickle.loads(pickle.dumps(self.basic_net))
elif self.box_type == 'black':
assert self.attack_net is not None, "should provide an additional net in black-box case"
aux_net = pickle.loads(pickle.dumps(self.basic_net))
aux_net.eval()
batch_size = inputs.size(0)
m = batch_size
n = batch_size
logits = aux_net(inputs)[0]
num_classes = logits.size(1)
outputs = aux_net(inputs)[0]
targets_prob = F.softmax(outputs.float(), dim=1)
y_tensor_adv = targets
step_sign = 1.0
x = inputs.detach()
x_org = x.detach()
x = x + torch.zeros_like(x).uniform_(-self.epsilon, self.epsilon)
if self.train_flag:
self.basic_net.train()
else:
self.basic_net.eval()
logits_pred_nat, fea_nat = aux_net(inputs)
num_classes = logits_pred_nat.size(1)
y_gt = one_hot_tensor(targets, num_classes, device)
loss_ce = softCrossEntropy()
iter_num = self.num_steps
for i in range(iter_num):
x.requires_grad_()
zero_gradients(x)
if x.grad is not None:
x.grad.data.fill_(0)
logits_pred, fea = aux_net(x)
ot_loss = ot.sinkhorn_loss_joint_IPOT(1, 0.00, logits_pred_nat,
logits_pred, None, None,
0.01, m, n)
aux_net.zero_grad()
adv_loss = ot_loss
adv_loss.backward(retain_graph=True)
x_adv = x.data + self.step_size * torch.sign(x.grad.data)
x_adv = torch.min(torch.max(x_adv, inputs - self.epsilon),
inputs + self.epsilon)
x_adv = torch.clamp(x_adv, -1.0, 1.0)
x = Variable(x_adv)
logits_pred, fea = self.basic_net(x)
self.basic_net.zero_grad()
y_sm = utils.label_smoothing(y_gt, y_gt.size(1), self.ls_factor)
adv_loss = loss_ce(logits_pred, y_sm.detach())
return logits_pred, adv_loss