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"""
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
https://arxiv.org/pdf/1906.04214.pdf
Tensorflow Implementation:
https://github.com/KaidiXu/GCN_ADV_Train
"""
import numpy as np
import scipy.sparse as sp
import torch
from torch.nn import functional as F
from torch.nn.parameter import Parameter
from tqdm import tqdm
import warnings
from deeprobust.graph import utils
from deeprobust.graph.global_attack import BaseAttack
class IGAttack(BaseAttack):
"""[Under Development] Untargeted Attack Version of IGAttack: IG-FGSM. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, https://arxiv.org/pdf/1903.01610.pdf.
Parameters
----------
model :
model to attack
nnodes : int
number of nodes in the input graph
feature_shape : tuple
shape of the input node features
attack_structure : bool
whether to attack graph structure
attack_features : bool
whether to attack node features
device: str
'cpu' or 'cuda'
"""
def __init__(self, model=None, nnodes=None, feature_shape=None, attack_structure=True, attack_features=False, device='cpu'):
super(IGAttack, self).__init__(model, nnodes, attack_structure, attack_features, device)
assert attack_features or attack_structure, 'attack_features or attack_structure cannot be both False'
self.modified_adj = None
self.modified_features = None
if attack_structure:
assert nnodes is not None, 'Please give nnodes='
self.adj_changes = Parameter(torch.FloatTensor(int(nnodes*(nnodes-1)/2)))
self.adj_changes.data.fill_(0)
if attack_features:
assert feature_shape is not None, 'Please give feature_shape='
self.feature_changes = Parameter(torch.FloatTensor(feature_shape))
self.feature_changes.data.fill_(0)
def attack(self, ori_features, ori_adj, labels, idx_train, n_perturbations, **kwargs):
"""Generate perturbations on the input graph.
Parameters
----------
ori_features :
Original (unperturbed) node feature matrix
ori_adj :
Original (unperturbed) adjacency matrix
labels :
node labels
idx_train :
node training indices
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
"""
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj, ori_features, labels, device=self.device)
victim_model.eval()
warnings.warn('This process is extremely slow!')
adj_norm = utils.normalize_adj_tensor(ori_adj)
s_e = self.calc_importance_edge(ori_features, adj_norm, labels, idx_train, steps=20)
s_f = self.calc_importance_feature(ori_features, adj_norm, labels, idx_train, steps=20)
import ipdb
ipdb.set_trace()
for t in tqdm(range(n_perturbations)):
modified_adj
self.adj_changes.data.copy_(torch.tensor(best_s))
self.modified_adj = self.get_modified_adj(ori_adj).detach()
def calc_importance_edge(self, features, adj, labels, idx_train, steps):
adj_norm = utils.normalize_adj_tensor(adj)
adj_norm.requires_grad = True
integrated_grad_list = []
for i in tqdm(range(adj.shape[0])):
for j in (range(adj.shape[1])):
if adj_norm[i][j]:
scaled_inputs = [(float(k)/ steps) * (adj_norm - 0) for k in range(0, steps + 1)]
else:
scaled_inputs = [-(float(k)/ steps) * (1 - adj_norm) for k in range(0, steps + 1)]
_sum = 0
# num_processes = steps
# # NOTE: this is required for the ``fork`` method to work
# self.surrogate.share_memory()
# processes = []
# for rank in range(num_processes):
# p = mp.Process(target=self.get_gradient, args=(features, scaled_inputs[rank], adj_norm, labels, idx_train))
# p.start()
# processes.append(p)
# for p in processes:
# p.join()
for new_adj in scaled_inputs:
output = self.surrogate(features, new_adj)
loss = F.nll_loss(output[idx_train], labels[idx_train])
# adj_grad = torch.autograd.grad(loss, adj[i][j], allow_unused=True)[0]
adj_grad = torch.autograd.grad(loss, adj_norm)[0]
adj_grad = adj_grad[i][j]
_sum += adj_grad
if adj_norm[i][j]:
avg_grad = (adj_norm[i][j] - 0) * _sum.mean()
else:
avg_grad = (1 - adj_norm[i][j]) * _sum.mean()
integrated_grad_list.append(avg_grad)
return integrated_grad_list
def get_gradient(self, features, new_adj, adj_norm, labels, idx_train):
output = self.surrogate(features, new_adj)
loss = F.nll_loss(output[idx_train], labels[idx_train])
# adj_grad = torch.autograd.grad(loss, adj[i][j], allow_unused=True)[0]
adj_grad = torch.autograd.grad(loss, adj_norm)[0]
adj_grad = adj_grad[i][j]
self._sum += adj_grad
def calc_importance_feature(self, features, adj_norm, labels, idx_train, steps):
features.requires_grad = True
integrated_grad_list = []
for i in range(features.shape[0]):
for j in range(features.shape[1]):
if features[i][j]:
scaled_inputs = [(float(k)/ steps) * (features - 0) for k in range(0, steps + 1)]
else:
scaled_inputs = [-(float(k)/ steps) * (1 - features) for k in range(0, steps + 1)]
_sum = 0
for new_features in scaled_inputs:
output = self.surrogate(new_features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
# adj_grad = torch.autograd.grad(loss, adj[i][j], allow_unused=True)[0]
feature_grad = torch.autograd.grad(loss, features, allow_unused=True)[0]
feature_grad = feature_grad[i][j]
_sum += feature_grad
if adj_norm[i][j]:
avg_grad = (features[i][j] - 0) * _sum.mean()
else:
avg_grad = (1 - features[i][j]) * _sum.mean()
integrated_grad_list.append(avg_grad)
return integrated_grad_list
def calc_gradient_adj(self, inputs, features):
for adj in inputs:
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = self.surrogate(features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, inputs)[0]
return adj_grad.mean()
def calc_gradient_feature(self, adj_norm, inputs):
for features in inputs:
output = self.surrogate(features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, inputs)[0]
return adj_grad.mean()
def get_modified_adj(self, ori_adj):
adj_changes_square = self.adj_changes - torch.diag(torch.diag(self.adj_changes, 0))
ind = np.diag_indices(self.adj_changes.shape[0])
adj_changes_symm = torch.clamp(adj_changes_square + torch.transpose(adj_changes_square, 1, 0), -1, 1)
modified_adj = adj_changes_symm + ori_adj
return modified_adj
def get_modified_features(self, ori_features):
return ori_features + self.feature_changes
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