Source code for textattack.attack_recipes.bae_garg_2019

"""
BAE (BAE: BERT-Based Adversarial Examples)
============================================

"""
from textattack.constraints.grammaticality import PartOfSpeech
from textattack.constraints.pre_transformation import (
    RepeatModification,
    StopwordModification,
)
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


[docs]class BAEGarg2019(AttackRecipe): """Siddhant Garg and Goutham Ramakrishnan, 2019. BAE: BERT-based Adversarial Examples for Text Classification. https://arxiv.org/pdf/2004.01970 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 """
[docs] @staticmethod def build(model_wrapper): # "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. constraints.append(PartOfSpeech(allow_verb_noun_swap=True)) # "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( threshold=0.936338023, metric="cosine", compare_against_original=True, window_size=15, skip_text_shorter_than_window=True, ) constraints.append(use_constraint) # # 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)