BAE (BAE: BERT-Based Adversarial Examples)
from textattack.constraints.grammaticality import PartOfSpeech
from textattack.constraints.pre_transformation import (
from textattack.constraints.semantics.sentence_encoders import UniversalSentenceEncoder
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import GreedyWordSwapWIR
from textattack.transformations import WordSwapMaskedLM
from .attack_recipe import AttackRecipe
"""Siddhant Garg and Goutham Ramakrishnan, 2019.
BAE: BERT-based Adversarial Examples for Text Classification.
This is "attack mode" 1 from the paper, BAE-R, word replacement.
We present 4 attack modes for BAE based on the
R and I operations, where for each token t in S:
• BAE-R: Replace token t (See Algorithm 1)
• BAE-I: Insert a token to the left or right of t
• BAE-R/I: Either replace token t or insert a
token to the left or right of t
• BAE-R+I: First replace token t, then insert a
token to the left or right of t
# "In this paper, we present a simple yet novel technique: BAE (BERT-based
# Adversarial Examples), which uses a language model (LM) for token
# replacement to best fit the overall context. We perturb an input sentence
# by either replacing a token or inserting a new token in the sentence, by
# means of masking a part of the input and using a LM to fill in the mask."
# We only consider the top K=50 synonyms from the MLM predictions.
# [from email correspondance with the author]
# "When choosing the top-K candidates from the BERT masked LM, we filter out
# the sub-words and only retain the whole words (by checking if they are
# present in the GloVE vocabulary)"
transformation = WordSwapMaskedLM(
method="bae", max_candidates=50, min_confidence=0.0
# Don't modify the same word twice or stopwords.
constraints = [RepeatModification(), StopwordModification()]
# For the R operations we add an additional check for
# grammatical correctness of the generated adversarial example by filtering
# out predicted tokens that do not form the same part of speech (POS) as the
# original token t_i in the sentence.
# "To ensure semantic similarity on introducing perturbations in the input
# text, we filter the set of top-K masked tokens (K is a pre-defined
# constant) predicted by BERT-MLM using a Universal Sentence Encoder (USE)
# (Cer et al., 2018)-based sentence similarity scorer."
# "[We] set a threshold of 0.8 for the cosine similarity between USE-based
# embeddings of the adversarial and input text."
# [from email correspondence with the author]
# "For a fair comparison of the benefits of using a BERT-MLM in our paper,
# we retained the majority of TextFooler's specifications. Thus we:
# 1. Use the USE for comparison within a window of size 15 around the word
# being replaced/inserted.
# 2. Set the similarity score threshold to 0.1 for inputs shorter than the
# window size (this translates roughly to almost always accepting the new text).
# 3. Perform the USE similarity thresholding of 0.8 with respect to the text
# just before the replacement/insertion and not the original text (For
# example: at the 3rd R/I operation, we compute the USE score on a window
# of size 15 of the text obtained after the first 2 R/I operations and not
# the original text).
# To address point (3) from above, compare the USE with the original text
# at each iteration instead of the current one (While doing this change
# for the R-operation is trivial, doing it for the I-operation with the
# window based USE comparison might be more involved)."
# Finally, since the BAE code is based on the TextFooler code, we need to
# adjust the threshold to account for the missing / pi in the cosine
# similarity comparison. So the final threshold is 1 - (1 - 0.8) / pi
# = 1 - (0.2 / pi) = 0.936338023.
use_constraint = UniversalSentenceEncoder(
# Goal is untargeted classification.
goal_function = UntargetedClassification(model_wrapper)
# "We estimate the token importance Ii of each token
# t_i ∈ S = [t1, . . . , tn], by deleting ti from S and computing the
# decrease in probability of predicting the correct label y, similar
# to (Jin et al., 2019).
# • "If there are multiple tokens can cause C to misclassify S when they
# replace the mask, we choose the token which makes Sadv most similar to
# the original S based on the USE score."
# • "If no token causes misclassification, we choose the perturbation that
# decreases the prediction probability P(C(Sadv)=y) the most."
search_method = GreedyWordSwapWIR(wir_method="delete")
return BAEGarg2019(goal_function, constraints, transformation, search_method)