def _attack(self, initial_result):
"""Calls the ``SearchMethod`` to perturb the ``AttackedText`` stored in
``initial_result``.
Args:
initial_result: The initial ``GoalFunctionResult`` from which to perturb.
Returns:
A ``SuccessfulAttackResult``, ``FailedAttackResult``,
or ``MaximizedAttackResult``.
"""
final_result = self.search_method(initial_result)
self.clear_cache()
if final_result.goal_status == GoalFunctionResultStatus.SUCCEEDED:
return SuccessfulAttackResult(
initial_result,
final_result,
)
elif final_result.goal_status == GoalFunctionResultStatus.SEARCHING:
return FailedAttackResult(
initial_result,
final_result,
)
elif final_result.goal_status == GoalFunctionResultStatus.MAXIMIZING:
return MaximizedAttackResult(
initial_result,
final_result,
)
else:
raise ValueError(
f"Unrecognized goal status {final_result.goal_status}")
def attack_one(self, original_tokenized_text, correct_output):
max_words_changed = min(self.max_words_changed,
len(original_tokenized_text.words))
original_result = self.goal_function.get_results(
[original_tokenized_text], correct_output)[0]
default_unswapped_word_indices = list(
range(len(original_tokenized_text.words)))
beam = [(original_tokenized_text, default_unswapped_word_indices)]
num_words_changed = 0
best_result = None
while num_words_changed < max_words_changed:
num_words_changed += 1
potential_next_beam = []
for text, unswapped_word_indices in beam:
transformations = self.get_transformations(
text,
indices_to_replace=unswapped_word_indices,
original_text=original_tokenized_text)
for next_text in transformations:
new_unswapped_word_indices = unswapped_word_indices.copy()
modified_word_index = next_text.attack_attrs[
'modified_word_index']
new_unswapped_word_indices.remove(modified_word_index)
potential_next_beam.append(
(next_text, new_unswapped_word_indices))
if len(potential_next_beam) == 0:
# If we did not find any possible perturbations, give up.
return FailedAttackResult(original_result)
transformed_text_candidates = [
text for (text, _) in potential_next_beam
]
results = self.goal_function.get_results(
transformed_text_candidates, correct_output)
scores = np.array([r.score for r in results])
# If we succeeded, break
best_result = results[scores.argmax()]
if best_result.succeeded:
break
# Otherwise, refill the beam. This works by sorting the scores
# in descending order and filling the beam from there.
best_indices = -scores.argsort()[:self.beam_width]
beam = [potential_next_beam[i] for i in best_indices]
if best_result is None:
return FailedAttackResult(original_result, best_result)
else:
return SuccessfulAttackResult(original_result, best_result)
def attack_one(self, initial_result):
"""
Calls the ``SearchMethod`` to perturb the ``TokenizedText`` stored in
``initial_result``.
Args:
initial_result: The initial ``GoalFunctionResult`` from which to perturb.
Returns:
Either a ``SuccessfulAttackResult`` or ``FailedAttackResult``.
"""
final_result = self.search_method(initial_result)
if final_result.succeeded:
return SuccessfulAttackResult(initial_result, final_result)
else:
return FailedAttackResult(initial_result, final_result)
def attack_one(self, tokenized_text, correct_output):
self.original_tokenized_text = tokenized_text
self.correct_output = correct_output
original_result = self.goal_function.get_results([tokenized_text],
correct_output)[0]
neighbors_len = self._get_neighbors_len(tokenized_text)
pop = self._generate_population(neighbors_len)
cur_score = original_result.score
for i in range(self.max_iters):
pop_results = self.goal_function.get_results(
[pm.tokenized_text for pm in pop], correct_output)
for idx, result in enumerate(pop_results):
pop[idx].result = pop_results[idx]
pop = sorted(pop, key=lambda x: -x.result.score)
print('\t\t', i, ' -- ', float(pop[0].result.score))
pop_scores = torch.Tensor([r.score for r in pop_results])
logits = ((-pop_scores) / self.temp).exp()
select_probs = (logits / logits.sum()).cpu().numpy()
if pop[0].result.succeeded:
return SuccessfulAttackResult(original_result, pop[0].result)
if pop[0].result.score > cur_score:
cur_score = pop[0].result.score
elif self.give_up_if_no_improvement:
break
elite = [pop[0]]
parent1_idx = np.random.choice(self.pop_size,
size=self.pop_size - 1,
p=select_probs)
parent2_idx = np.random.choice(self.pop_size,
size=self.pop_size - 1,
p=select_probs)
children = [
self._crossover(pop[parent1_idx[idx]], pop[parent2_idx[idx]])
for idx in range(self.pop_size - 1)
]
for c in children:
self._perturb(c)
pop = elite + children
return FailedAttackResult(original_result, pop[0].result)
def attack_one(self, tokenized_text, correct_output):
original_tokenized_text = tokenized_text
num_words_changed = 0
# Sort words by order of importance
original_result = self.goal_function.get_results([tokenized_text],
correct_output)[0]
cur_score = original_result.score
len_text = len(tokenized_text.words)
leave_one_texts = \
[tokenized_text.replace_word_at_index(i,self.replacement_str) for i in range(len_text)]
leave_one_scores = np.array([result.score for result in \
self.goal_function.get_results(leave_one_texts, correct_output)])
index_order = (-leave_one_scores).argsort()
new_tokenized_text = None
new_text_label = None
i = 0
while ((self.max_depth is None) or
num_words_changed <= self.max_depth) and i < len(index_order):
transformed_text_candidates = self.get_transformations(
tokenized_text,
original_tokenized_text,
indices_to_replace=[index_order[i]])
i += 1
if len(transformed_text_candidates) == 0:
continue
num_words_changed += 1
results = sorted(self.goal_function.get_results(
transformed_text_candidates, correct_output),
key=lambda x: -x.score)
# Skip swaps which don't improve the score
if results[0].score > cur_score:
cur_score = results[0].score
else:
continue
# If we succeeded, return the index with best similarity.
if results[0].succeeded:
best_result = results[0]
# @TODO: Use vectorwise operations
max_similarity = -float('inf')
for result in results:
if not result.succeeded:
break
candidate = result.tokenized_text
try:
similarity_score = candidate.attack_attrs[
'similarity_score']
except KeyError:
# If the attack was run without any similarity metrics,
# candidates won't have a similarity score. In this
# case, break and return the candidate that changed
# the original score the most.
break
if similarity_score > max_similarity:
max_similarity = similarity_score
best_result = result
return SuccessfulAttackResult(original_result, best_result)
else:
tokenized_text = results[0].tokenized_text
if len(results):
return FailedAttackResult(original_result, results[0])
else:
return FailedAttackResult(original_result)