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"""
Pruthi2019: Combating with Robust Word Recognition
=================================================================
"""
from textattack import Attack
from textattack.constraints.overlap import MaxWordsPerturbed
from textattack.constraints.pre_transformation import (
MinWordLength,
RepeatModification,
StopwordModification,
)
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import GreedySearch
from textattack.transformations import (
CompositeTransformation,
WordSwapNeighboringCharacterSwap,
WordSwapQWERTY,
WordSwapRandomCharacterDeletion,
WordSwapRandomCharacterInsertion,
)
from .attack_recipe import AttackRecipe
class Pruthi2019(AttackRecipe):
"""An implementation of the attack used in "Combating Adversarial
Misspellings with Robust Word Recognition", Pruthi et al., 2019.
This attack focuses on a small number of character-level changes that simulate common typos. It combines:
- Swapping neighboring characters
- Deleting characters
- Inserting characters
- Swapping characters for adjacent keys on a QWERTY keyboard.
https://arxiv.org/abs/1905.11268
:param model: Model to attack.
:param max_num_word_swaps: Maximum number of modifications to allow.
"""
@staticmethod
def build(model_wrapper, max_num_word_swaps=1):
# a combination of 4 different character-based transforms
# ignore the first and last letter of each word, as in the paper
transformation = CompositeTransformation(
[
WordSwapNeighboringCharacterSwap(
random_one=False, skip_first_char=True, skip_last_char=True
),
WordSwapRandomCharacterDeletion(
random_one=False, skip_first_char=True, skip_last_char=True
),
WordSwapRandomCharacterInsertion(
random_one=False, skip_first_char=True, skip_last_char=True
),
WordSwapQWERTY(
random_one=False, skip_first_char=True, skip_last_char=True
),
]
)
# only edit words of length >= 4, edit max_num_word_swaps words.
# note that we also are not editing the same word twice, so
# max_num_word_swaps is really the max number of character
# changes that can be made. The paper looks at 1 and 2 char attacks.
constraints = [
MinWordLength(min_length=4),
StopwordModification(),
MaxWordsPerturbed(max_num_words=max_num_word_swaps),
RepeatModification(),
]
# untargeted attack
goal_function = UntargetedClassification(model_wrapper)
search_method = GreedySearch()
return Attack(goal_function, constraints, transformation, search_method)
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