Source code for textattack.attack_recipes.seq2sick_cheng_2018_blackbox


(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 (
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 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)