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attack_interactive.py
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attack_interactive.py
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import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import model
import scoring
import scoring_char
import transformer
import transformer_char
import numpy as np
import pickle
default_filter = '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n'
def recoveradv(rawsequence, index2word, inputs, advwords):
filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n '
rear_ct = len(rawsequence)
advsequence = rawsequence[:]
try:
for i in range(inputs.size()[0]-1,-1,-1):
wordi = index2word[inputs[i].item()]
rear_ct = rawsequence[:rear_ct].rfind(wordi)
# print(rear_ct)
if inputs[i].item()>=3:
advsequence = advsequence[:rear_ct] + advwords[i] + advsequence[rear_ct + len(wordi):]
except:
print('something went wrong')
return advsequence
def simple_tokenize(input_seq, dict_word, filters= default_filter):
input_seq = input_seq.lower()
translate_dict = dict((c, ' ') for c in filters)
translate_map = str.maketrans(translate_dict)
text = input_seq.translate(translate_map)
seq = text.strip().split(' ')
index_seq = []
for i in seq:
if i in dict_word:
if dict_word[i]+3<20000:
index_seq.append(dict_word[i]+3)
else:
index_seq.append(2)
else:
index_seq.append(2)
return index_seq
default_alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:\'\"/\\|_@#$%^&*~`+ =<>()[]{}"
def transchar(x, alphabet=default_alphabet,length=1014):
inputs = torch.zeros(1,len(alphabet),length)
for j, ch in enumerate(x[::-1]):
if j>=length:
break
if ch in alphabet:
inputs[0,alphabet.find(ch),j] = 1.0
return inputs
def visualize(input_str, dict_word=[],index2word = [], classes_list = [], power=5, scoring_alg = 'replaceone', transformer_alg = 'homoglyph', model = model, mode = 'word', maxlength = 500, device = None, filter_char = default_filter, alphabet = default_alphabet):
numclass = len(classes_list)
if mode=='word':
input_seq = simple_tokenize(input_str, dict_word)
input_seq = torch.Tensor(input_seq).long().view(1,-1)
if device:
input_seq = input_seq.to(device)
res1 = model(input_seq)
pred1 = torch.max(res1, 1)[1].view(-1)
losses = scoring.scorefunc(scoring_alg)(model, input_seq, pred1, numclass)
print(input_str)
pred1 = pred1.item()
print('original:',classes_list[pred1])
sorted, indices = torch.sort(losses,dim = 1,descending=True)
advinputs = input_seq.clone()
wtmp = []
for i in range(input_seq.size()[1]):
if advinputs[0,i].item()>3:
wtmp.append(index2word[advinputs[0,i].item()])
else:
wtmp.append('')
j = 0
t = 0
while j < power and t<input_seq.size()[1]:
if advinputs[0,indices[0][t]].item()>3:
word, advinputs[0,indices[0][t]] = transformer.transform(transformer_alg)(advinputs[0,indices[0][t]].item(),dict_word, index2word, top_words = 20000)
wtmp[indices[0][t]] = word
j+=1
t+=1
output2 = model(advinputs)
pred2 = torch.max(output2, 1)[1].view(-1).item()
adv_str = recoveradv(input_str.lower(), index2word, input_seq[0], wtmp)
print(adv_str)
print('adversarial:', classes_list[pred2])
return (input_str, torch.exp(res1).detach().cpu()[0], classes_list[pred1], adv_str, torch.exp(output2).detach().cpu()[0], classes_list[pred2])
elif mode=='char':
inputs = transchar(input_str, alphabet = alphabet)
if device:
inputs = inputs.to(device)
output = model(inputs)
pred1 = torch.max(output, 1)[1].view(-1)
losses = scoring_char.scorefunc(scoring_alg)(model, inputs, pred1, numclass)
sorted, indices = torch.sort(losses,dim = 1,descending=True)
advinputs = inputs.clone()
dt = inputs.sum(dim=1).int()
j=0
t=0
md = input_str.lower()[:][::-1]
while j < power and t<inputs.size()[2]:
if dt[0,indices[0][t]].item()>0:
advinputs[0,:,indices[0][t]],nowchar = transformer_char.transform(transformer_alg)(inputs, torch.max(advinputs[0,:,indices[0][t]],0)[1].item(), alphabet)
md = md[:indices[0][t].item()] + nowchar + md[indices[0][t].item()+1:]
j+=1
t+=1
md = md[::-1]
output2 = model(advinputs)
pred2 = torch.max(output2, 1)[1].view(-1)
print(input_str)
print('original:',classes_list[pred1.item()])
print(md)
print('adversarial:', classes_list[pred2.item()])
return (input_str, torch.exp(output)[0].detach().cpu(), classes_list[pred1.item()], md, torch.exp(output2)[0].detach().cpu(), classes_list[pred2.item()])
else:
raise Exception('Wrong mode %s' % mode)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Data')
parser.add_argument('--data', type=int, default=1, metavar='N',
help='data 0 - 7: Default: Amazon Review Full, classify the attitude 1-5 of the review')
parser.add_argument('--model', type=str, default='simplernn', metavar='N',
help='model type: LSTM as default')
parser.add_argument('--modelpath', type=str, default='', metavar='N',
help='model file path')
args = parser.parse_args()
if not args.modelpath:
args.modelpath = 'models/%s_%d_bestmodel.dat' % (args.model,args.data)
torch.manual_seed(8)
torch.cuda.manual_seed(8)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model == "charcnn":
args.datatype = "char"
elif args.model == "simplernn":
args.datatype = "word"
info = pickle.load(open('dict/'+str(args.data)+'.info','rb'))
word_index = info['word_index']
index2word = info['index2word']
classes_list = info['classes_list']
numclass = len(classes_list)
if args.model == "charcnn":
model = model.CharCNN(classes = numclass)
elif args.model == "simplernn":
model = model.smallRNN(classes = numclass)
elif args.model == "bilstm":
model = model.smallRNN(classes = numclass, bidirection = True)
print(model)
state = torch.load(args.modelpath)
model = model.to(device)
try:
model.load_state_dict(state['state_dict'])
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(state['state_dict'])
model = model.module
print('Type input:')
s = input()
while s:
visualize(s, power=2, mode=args.datatype, model = model, dict_word = word_index, index2word = index2word, classes_list = classes_list, device = device)
print('Type next input(Enter to exit):')
s = input()