Source code for textattack.attack_recipes.genetic_algorithm_alzantot_2018

"""

Alzantot Genetic Algorithm
=======================================
(Generating Natural Language Adversarial Examples)

.. warning::
    This attack uses a very slow language model. Consider using the ``faster-alzantot``
    recipe instead.

"""

from textattack import Attack
from textattack.constraints.grammaticality.language_models import (
    Google1BillionWordsLanguageModel,
)
from textattack.constraints.overlap import MaxWordsPerturbed
from textattack.constraints.pre_transformation import (
    InputColumnModification,
    RepeatModification,
    StopwordModification,
)
from textattack.constraints.semantics import WordEmbeddingDistance
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import AlzantotGeneticAlgorithm
from textattack.transformations import WordSwapEmbedding

from .attack_recipe import AttackRecipe


[docs]class GeneticAlgorithmAlzantot2018(AttackRecipe): """Alzantot, M., Sharma, Y., Elgohary, A., Ho, B., Srivastava, M.B., & Chang, K. (2018). Generating Natural Language Adversarial Examples. https://arxiv.org/abs/1804.07998 """
[docs] @staticmethod def build(model_wrapper): # # Swap words with their embedding nearest-neighbors. # # Embedding: Counter-fitted Paragram Embeddings. # # "[We] fix the hyperparameter values to S = 60, N = 8, K = 4, and δ = 0.5" # transformation = WordSwapEmbedding(max_candidates=8) # # Don't modify the same word twice or stopwords # constraints = [RepeatModification(), StopwordModification()] # # During entailment, we should only edit the hypothesis - keep the premise # the same. # input_column_modification = InputColumnModification( ["premise", "hypothesis"], {"premise"} ) constraints.append(input_column_modification) # # Maximum words perturbed percentage of 20% # constraints.append(MaxWordsPerturbed(max_percent=0.2)) # # Maximum word embedding euclidean distance of 0.5. # constraints.append( WordEmbeddingDistance(max_mse_dist=0.5, compare_against_original=False) ) # # Language Model # constraints.append( Google1BillionWordsLanguageModel( top_n_per_index=4, compare_against_original=False ) ) # # Goal is untargeted classification # goal_function = UntargetedClassification(model_wrapper) # # Perform word substitution with a genetic algorithm. # search_method = AlzantotGeneticAlgorithm( pop_size=60, max_iters=20, post_crossover_check=False ) return Attack(goal_function, constraints, transformation, search_method)