def heuristic_fn(text, candidate):
'''
Return the difference between the classification probability of the original
word and the candidate substitute synonym, which is defined in Eq.(4) and Eq.(5).
'''
doc = nlp(text)
origin_vector = None
perturbed_vector = None
if level == 'word':
origin_vector = text_to_vector(text, tokenizer, dataset)
perturbed_tokens = _compile_perturbed_tokens(doc, [candidate])
perturbed_doc = nlp(' '.join(perturbed_tokens))
perturbed_vector = text_to_vector(perturbed_doc.text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
origin_vector = doc_process(text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_tokens = _compile_perturbed_tokens(
nlp(input_text), [candidate])
perturbed_text = ' '.join(perturbed_tokens)
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
perturbed_prob = grad_guide.predict_prob(input_vector=perturbed_vector)
delta_p = origin_prob[true_y] - perturbed_prob[true_y]
return delta_p
def evaluate_word_saliency(doc, grad_guide, tokenizer, input_y, dataset,
level):
word_saliency_list = []
# zero the code of the current word and calculate the amount of change in the classification probability
if level == 'word':
max_len = config.word_max_len[dataset]
text = [doc[position].text for position in range(len(doc))]
text = ' '.join(text)
origin_vector = text_to_vector(text, tokenizer, dataset)
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
for position in range(len(doc)):
if position >= max_len:
break
# get x_i^(\hat)
without_word_vector = copy.deepcopy(origin_vector)
without_word_vector[0][position] = 0
prob_without_word = grad_guide.predict_prob(
input_vector=without_word_vector)
# calculate S(x,w_i) defined in Eq.(6)
word_saliency = origin_prob[input_y] - prob_without_word[input_y]
word_saliency_list.append(
(position, doc[position], word_saliency, doc[position].tag_))
elif level == 'char':
max_len = config.char_max_len[dataset]
embedding_dic = get_embedding_dict()
origin_vector = doc_process(doc.text.lower(), embedding_dic,
dataset).reshape(1, max_len)
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
find_a_word = False
word_position = 0
without_word_vector = copy.deepcopy(origin_vector)
for i, c in enumerate(doc.text):
if i >= max_len:
break
if c is not ' ':
without_word_vector[0][i] = 0
else:
find_a_word = True
prob_without_word = grad_guide.predict_prob(
without_word_vector)
word_saliency = origin_prob[input_y] - prob_without_word[
input_y]
word_saliency_list.append(
(word_position, doc[word_position], word_saliency,
doc[word_position].tag_))
word_position += 1
if find_a_word:
without_word_vector = copy.deepcopy(origin_vector)
find_a_word = False
position_word_list = []
for word in word_saliency_list:
position_word_list.append((word[0], word[1]))
return position_word_list, word_saliency_list
def origin_perturbed_vector_fn(text, substitute):
doc = nlp(text)
origin_vector = None
perturbed_vector = None
if level == 'word':
origin_vector = text_to_vector(text, tokenizer, dataset)
perturbed_tokens = _compile_perturbed_tokens(doc, substitute)
perturbed_doc = nlp(' '.join(perturbed_tokens))
perturbed_vector = text_to_vector(perturbed_doc.text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
origin_vector = doc_process(text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_tokens = _compile_perturbed_tokens(
nlp(input_text), substitute)
perturbed_text = ' '.join(perturbed_tokens)
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
return origin_vector, perturbed_vector
def halt_condition_fn(perturbed_text):
'''
Halt if model output is changed.
'''
perturbed_vector = None
if level == 'word':
perturbed_vector = text_to_vector(perturbed_text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
adv_y = grad_guide.predict_classes(input_vector=perturbed_vector)
if adv_y != true_y:
return True
else:
return False
def adversarial_paraphrase(input_text,
true_y,
grad_guide,
tokenizer,
dataset,
dataset_dict,
word_candidate,
level,
verbose=True):
'''
Compute a perturbation, greedily choosing the synonym if it causes the most
significant change in the classification probability after replacement
:return perturbed_text: generated adversarial examples
:return perturbed_y: predicted class of perturbed_text
:return sub_rate: word replacement rate showed in Table 3
:return change_tuple_list: list of substitute words
'''
def halt_condition_fn(perturbed_text):
'''
Halt if model output is changed.
'''
perturbed_vector = None
if level == 'word':
perturbed_vector = text_to_vector(perturbed_text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
adv_y = grad_guide.predict_classes(input_vector=perturbed_vector)
if adv_y != true_y:
return True
else:
return False
def heuristic_fn(text, candidate):
'''
Return the difference between the classification probability of the original
word and the candidate substitute synonym, which is defined in Eq.(4) and Eq.(5).
'''
doc = nlp(text)
origin_vector = None
perturbed_vector = None
if level == 'word':
origin_vector = text_to_vector(text, tokenizer, dataset)
perturbed_tokens = _compile_perturbed_tokens(doc, [candidate])
perturbed_doc = nlp(' '.join(perturbed_tokens))
perturbed_vector = text_to_vector(perturbed_doc.text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
origin_vector = doc_process(text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_tokens = _compile_perturbed_tokens(
nlp(input_text), [candidate])
perturbed_text = ' '.join(perturbed_tokens)
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
perturbed_prob = grad_guide.predict_prob(input_vector=perturbed_vector)
delta_p = origin_prob[true_y] - perturbed_prob[true_y]
return delta_p
def origin_perturbed_vector_fn(text, substitute):
doc = nlp(text)
origin_vector = None
perturbed_vector = None
if level == 'word':
origin_vector = text_to_vector(text, tokenizer, dataset)
perturbed_tokens = _compile_perturbed_tokens(doc, substitute)
perturbed_doc = nlp(' '.join(perturbed_tokens))
perturbed_vector = text_to_vector(perturbed_doc.text, tokenizer,
dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
origin_vector = doc_process(text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_tokens = _compile_perturbed_tokens(
nlp(input_text), substitute)
perturbed_text = ' '.join(perturbed_tokens)
perturbed_vector = doc_process(perturbed_text,
get_embedding_dict(),
dataset).reshape(1, max_len)
return origin_vector, perturbed_vector
def delta_P_fn(origin_vector, perturbed_vector):
'''Return the difference between the classification probility of
the clean text and the perturbed text.
'''
# origin_vector = text_to_vector(text, tokenizer, dataset)
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
# perturbed_vector = text_to_vector(perturbed_text, tokenizer, dataset)
perturbed_prob = grad_guide.predict_prob(input_vector=perturbed_vector)
delta_P = origin_prob[true_y] - perturbed_prob[true_y]
return delta_P
doc = nlp(input_text)
# BU-MHS
position_word_list, word_saliency_list = evaluate_word_saliency(
doc, grad_guide, tokenizer, true_y, dataset, level)
perturbed_text, sub_word, sub_rate, NE_rate, change_tuple_list = BU_MHS(
doc,
true_y,
dataset,
dataset_dict,
word_candidate,
word_saliency_list=word_saliency_list,
heuristic_fn=heuristic_fn,
halt_condition_fn=halt_condition_fn,
origin_perturbed_vector_fn=origin_perturbed_vector_fn,
delta_P_fn=delta_P_fn,
verbose=verbose)
# print("perturbed_text after perturb_text:", perturbed_text)
origin_vector = perturbed_vector = None
if level == 'word':
origin_vector = text_to_vector(input_text, tokenizer, dataset)
perturbed_vector = text_to_vector(perturbed_text, tokenizer, dataset)
elif level == 'char':
max_len = config.char_max_len[dataset]
origin_vector = doc_process(input_text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_vector = doc_process(perturbed_text, get_embedding_dict(),
dataset).reshape(1, max_len)
perturbed_y = grad_guide.predict_classes(input_vector=perturbed_vector)
if verbose:
origin_prob = grad_guide.predict_prob(input_vector=origin_vector)
perturbed_prob = grad_guide.predict_prob(input_vector=perturbed_vector)
raw_score = origin_prob[true_y] - perturbed_prob[true_y]
print('Prob before: ', origin_prob[true_y], '. Prob after: ',
perturbed_prob[true_y], '. Prob shift: ', raw_score)
return perturbed_text, perturbed_y, sub_word, sub_rate, NE_rate, change_tuple_list