Source code for textattack.attack_recipes.hotflip_ebrahimi_2017

"""

HotFlip
===========
(HotFlip: White-Box Adversarial Examples for Text Classification)

"""
from textattack import Attack
from textattack.constraints.grammaticality import PartOfSpeech
from textattack.constraints.overlap import MaxWordsPerturbed
from textattack.constraints.pre_transformation import (
    RepeatModification,
    StopwordModification,
)
from textattack.constraints.semantics import WordEmbeddingDistance
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import BeamSearch
from textattack.transformations import WordSwapGradientBased

from .attack_recipe import AttackRecipe


[docs]class HotFlipEbrahimi2017(AttackRecipe): """Ebrahimi, J. et al. (2017) HotFlip: White-Box Adversarial Examples for Text Classification https://arxiv.org/abs/1712.06751 This is a reproduction of the HotFlip word-level attack (section 5 of the paper). """
[docs] @staticmethod def build(model_wrapper): # # "HotFlip ... uses the gradient with respect to a one-hot input # representation to efficiently estimate which individual change has the # highest estimated loss." transformation = WordSwapGradientBased(model_wrapper, top_n=1) # # Don't modify the same word twice or stopwords # constraints = [RepeatModification(), StopwordModification()] # # 0. "We were able to create only 41 examples (2% of the correctly- # classified instances of the SST test set) with one or two flips." # constraints.append(MaxWordsPerturbed(max_num_words=2)) # # 1. "The cosine similarity between the embedding of words is bigger than a # threshold (0.8)." # constraints.append(WordEmbeddingDistance(min_cos_sim=0.8)) # # 2. "The two words have the same part-of-speech." # constraints.append(PartOfSpeech()) # # Goal is untargeted classification # goal_function = UntargetedClassification(model_wrapper) # # "HotFlip ... uses a beam search to find a set of manipulations that work # well together to confuse a classifier ... The adversary uses a beam size # of 10." # search_method = BeamSearch(beam_width=10) return Attack(goal_function, constraints, transformation, search_method)