Spaces:

anonymous8
/

Rapid-Textual-Adversarial-Defense

Running

Rapid-Textual-Adversarial-Defense / textattack /attack_recipes /pruthi_2019.py

anonymous8/RPD-Demo

initial commit

4943752 over 1 year ago

No virus

2.9 kB

	"""
	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)