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Rapid-Textual-Adversarial-Defense / textattack /attack_recipes /deepwordbug_gao_2018.py

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	"""

	DeepWordBug
	========================================
	(Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers)

	"""

	from textattack import Attack
	from textattack.constraints.overlap import LevenshteinEditDistance
	from textattack.constraints.pre_transformation import (
	RepeatModification,
	StopwordModification,
	)
	from textattack.goal_functions import UntargetedClassification
	from textattack.search_methods import GreedyWordSwapWIR
	from textattack.transformations import (
	CompositeTransformation,
	WordSwapNeighboringCharacterSwap,
	WordSwapRandomCharacterDeletion,
	WordSwapRandomCharacterInsertion,
	WordSwapRandomCharacterSubstitution,
	)

	from .attack_recipe import AttackRecipe


	class DeepWordBugGao2018(AttackRecipe):
	"""Gao, Lanchantin, Soffa, Qi.

	Black-box Generation of Adversarial Text Sequences to Evade Deep Learning
	Classifiers.

	https://arxiv.org/abs/1801.04354
	"""

	@staticmethod
	def build(model_wrapper, use_all_transformations=True):
	#
	# Swap characters out from words. Choose the best of four potential transformations.
	#
	if use_all_transformations:
	# We propose four similar methods:
	transformation = CompositeTransformation(
	[
	# (1) Swap: Swap two adjacent letters in the word.
	WordSwapNeighboringCharacterSwap(),
	# (2) Substitution: Substitute a letter in the word with a random letter.
	WordSwapRandomCharacterSubstitution(),
	# (3) Deletion: Delete a random letter from the word.
	WordSwapRandomCharacterDeletion(),
	# (4) Insertion: Insert a random letter in the word.
	WordSwapRandomCharacterInsertion(),
	]
	)
	else:
	# We use the Combined Score and the Substitution Transformer to generate
	# adversarial samples, with the maximum edit distance difference of 30
	# (ϵ = 30).
	transformation = WordSwapRandomCharacterSubstitution()
	#
	# Don't modify the same word twice or stopwords
	#
	constraints = [RepeatModification(), StopwordModification()]
	#
	# In these experiments, we hold the maximum difference
	# on edit distance (ϵ) to a constant 30 for each sample.
	#
	constraints.append(LevenshteinEditDistance(30))
	#
	# Goal is untargeted classification
	#
	goal_function = UntargetedClassification(model_wrapper)
	#
	# Greedily swap words with "Word Importance Ranking".
	#
	search_method = GreedyWordSwapWIR()

	return Attack(goal_function, constraints, transformation, search_method)