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"""
Kuleshov2017
==============
(Adversarial Examples for Natural Language Classification Problems)
"""
from textattack import Attack
from textattack.constraints.grammaticality.language_models import GPT2
from textattack.constraints.overlap import MaxWordsPerturbed
from textattack.constraints.pre_transformation import (
RepeatModification,
StopwordModification,
)
from textattack.constraints.semantics.sentence_encoders import ThoughtVector
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import GreedySearch
from textattack.transformations import WordSwapEmbedding
from .attack_recipe import AttackRecipe
class Kuleshov2017(AttackRecipe):
"""Kuleshov, V. et al.
Generating Natural Language Adversarial Examples.
https://openreview.net/pdf?id=r1QZ3zbAZ.
"""
@staticmethod
def build(model_wrapper):
#
# "Specifically, in all experiments, we used a target of τ = 0.7,
# a neighborhood size of N = 15, and parameters λ_1 = 0.2 and δ = 0.5; we set
# the syntactic bound to λ_2 = 2 nats for sentiment analysis"
#
# Word swap with top-15 counter-fitted embedding neighbors.
#
transformation = WordSwapEmbedding(max_candidates=15)
#
# Don't modify the same word twice or stopwords
#
constraints = [RepeatModification(), StopwordModification()]
#
# Maximum of 50% of words perturbed (δ in the paper).
#
constraints.append(MaxWordsPerturbed(max_percent=0.5))
#
# Maximum thought vector Euclidean distance of λ_1 = 0.2. (eq. 4)
#
constraints.append(ThoughtVector(threshold=0.2, metric="max_euclidean"))
#
#
# Maximum language model log-probability difference of λ_2 = 2. (eq. 5)
#
constraints.append(GPT2(max_log_prob_diff=2.0))
#
# Goal is untargeted classification: reduce original probability score
# to below τ = 0.7 (Algorithm 1).
#
goal_function = UntargetedClassification(model_wrapper, target_max_score=0.7)
#
# Perform word substitution with a genetic algorithm.
#
search_method = GreedySearch()
return Attack(goal_function, constraints, transformation, search_method)
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