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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)
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