-
Notifications
You must be signed in to change notification settings - Fork 5
/
generate_target_attack.py
154 lines (130 loc) · 5.3 KB
/
generate_target_attack.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
#%%
import torch
import numpy as np
import torch.nn.functional as F
import torch.optim as optim
from deeprobust.graph.defense import GCN
from deeprobust.graph.targeted_attack import Nettack
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--dataset', type=str, default='Pubmed', choices=['cora', 'cora_ml', 'citeseer','Pubmed'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.15, help='pertubation rate')
parser.add_argument("--label_rate", type=float, default=0.1, help='rate of labeled data')
args = parser.parse_known_args()[0]
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
np.random.seed(15)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
idx_train = idx_train[:int(args.label_rate * adj.shape[0])]
# Setup Surrogate model
surrogate = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1,
nhid=16, dropout=0, with_relu=False, with_bias=False, device=device)
surrogate = surrogate.to(device)
surrogate.fit(features, adj, labels, idx_train, idx_val, patience=30)
def test(adj, features, target_node):
''' test on GCN '''
gcn = GCN(nfeat=features.shape[1],
nhid=16,
nclass=labels.max().item() + 1,
dropout=0.5, device=device)
gcn = gcn.to(device)
gcn.fit(features, adj, labels, idx_train, idx_val, patience=10)
gcn.eval()
output = gcn.predict()
probs = torch.exp(output[[target_node]])[0]
print('Target node probs: {}'.format(probs.detach().cpu().numpy()))
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Overall test set results:",
"accuracy= {:.4f}".format(acc_test.item()))
return acc_test.item()
def multi_test_poison():
# test on 40 nodes on poisoining attack
cnt = 0
degrees = adj.sum(0).A1
np.random.seed(42)
idx = np.arange(0,adj.shape[0])
np.random.shuffle(idx)
node_list = idx[:int(args.ptb_rate*len(idx))]
num = len(node_list)
print('=== [Poisoning] Attacking %s nodes respectively ===' % num)
modified_adj = adj
for target_node in tqdm(node_list):
n_perturbations = int(degrees[target_node])
model = Nettack(surrogate, nnodes=adj.shape[0], attack_structure=True, attack_features=False, device=device)
model = model.to(device)
model.attack(features, modified_adj, labels, target_node, n_perturbations, verbose=False)
modified_adj = model.modified_adj
modified_features = model.modified_features
acc = single_test(modified_adj, modified_features, target_node)
if acc == 0:
cnt += 1
print('misclassification rate : %s' % (cnt/num))
import os
import scipy.sparse as sp
path = os.path.join("./data/{}".format(args.label_rate),"nettack/")
if not os.path.exists(path):
os.makedirs(path)
file_path = os.path.join(path,"{}.npz".format(args.dataset))
if type(modified_adj) is torch.Tensor:
sparse_adj = to_scipy(modified_adj)
sp.save_npz(file_path, sparse_adj)
else:
sp.save_npz(file_path, modified_adj)
def single_test(adj, features, target_node, gcn=None):
if gcn is None:
# test on GCN (poisoning attack)
gcn = GCN(nfeat=features.shape[1],
nhid=16,
nclass=labels.max().item() + 1,
dropout=0.5, device=device)
gcn = gcn.to(device)
gcn.fit(features, adj, labels, idx_train, idx_val, patience=30)
gcn.eval()
output = gcn.predict()
else:
# test on GCN (evasion attack)
output = gcn.predict(features, adj)
probs = torch.exp(output[[target_node]])
# acc_test = accuracy(output[[target_node]], labels[target_node])
acc_test = (output.argmax(1)[target_node] == labels[target_node])
return acc_test.item()
#%%
cnt = 0
degrees = adj.sum(0).A1
np.random.seed(42)
idx = np.arange(0,adj.shape[0])
np.random.shuffle(idx)
node_list = idx[:int(args.ptb_rate*len(idx))]
# node_list=[0]
modified_adj = adj
num = len(node_list)
print('=== [Poisoning] Attacking %s nodes respectively ===' % num)
for target_node in tqdm(node_list):
n_perturbations = int(degrees[target_node])
model = Nettack(surrogate, nnodes=adj.shape[0], attack_structure=True, attack_features=False, device=device)
model = model.to(device)
model.attack(features, modified_adj, labels, target_node, n_perturbations, verbose=False)
modified_adj = model.modified_adj
modified_features = model.modified_features
# acc = single_test(modified_adj, modified_features, target_node)
# if acc == 0:
# cnt += 1
print('misclassification rate : %s' % (cnt/num))
#%%
import os
import scipy.sparse as sp
path = os.path.join("./data/{}".format(args.label_rate),"nettack/")
if not os.path.exists(path):
os.makedirs(path)
file_path = os.path.join(path,"{}.npz".format(args.dataset))
sp.save_npz(file_path, modified_adj.tocsr())