"""
Seq2Sick
================================================
(Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples)
"""
from textattack import Attack
from textattack.constraints.overlap import LevenshteinEditDistance
from textattack.constraints.pre_transformation import (
RepeatModification,
StopwordModification,
)
from textattack.goal_functions import NonOverlappingOutput
from textattack.search_methods import GreedyWordSwapWIR
from textattack.transformations import WordSwapEmbedding
from .attack_recipe import AttackRecipe
[docs]class Seq2SickCheng2018BlackBox(AttackRecipe):
"""Cheng, Minhao, et al.
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with
Adversarial Examples
https://arxiv.org/abs/1803.01128
This is a greedy re-implementation of the seq2sick attack method. It does
not use gradient descent.
"""
[docs] @staticmethod
def build(model_wrapper, goal_function="non_overlapping"):
#
# Goal is non-overlapping output.
#
goal_function = NonOverlappingOutput(model_wrapper)
transformation = WordSwapEmbedding(max_candidates=50)
#
# 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))
#
# Greedily swap words with "Word Importance Ranking".
#
search_method = GreedyWordSwapWIR(wir_method="unk")
return Attack(goal_function, constraints, transformation, search_method)