Source code for textattack.attack_recipes.deepwordbug_gao_2018


(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 (
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import GreedyWordSwapWIR
from textattack.transformations import (

from .attack_recipe import AttackRecipe

[docs]class DeepWordBugGao2018(AttackRecipe): """Gao, Lanchantin, Soffa, Qi. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. """
[docs] @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)