Multi-granularity Textual Adversarial Attack with Behavior Cloning
- URL: http://arxiv.org/abs/2109.04367v1
- Date: Thu, 9 Sep 2021 15:46:45 GMT
- Title: Multi-granularity Textual Adversarial Attack with Behavior Cloning
- Authors: Yangyi Chen, Jin Su, Wei Wei
- Abstract summary: We propose MAYA, a Multi-grAnularitY Attack model to generate high-quality adversarial samples with fewer queries to victim models.
We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets.
- Score: 4.727534308759158
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recently, the textual adversarial attack models become increasingly popular
due to their successful in estimating the robustness of NLP models. However,
existing works have obvious deficiencies. (1) They usually consider only a
single granularity of modification strategies (e.g. word-level or
sentence-level), which is insufficient to explore the holistic textual space
for generation; (2) They need to query victim models hundreds of times to make
a successful attack, which is highly inefficient in practice. To address such
problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to
effectively generate high-quality adversarial samples with fewer queries to
victim models. Furthermore, we propose a reinforcement-learning based method to
train a multi-granularity attack agent through behavior cloning with the expert
knowledge from our MAYA algorithm to further reduce the query times.
Additionally, we also adapt the agent to attack black-box models that only
output labels without confidence scores. We conduct comprehensive experiments
to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two
different black-box attack settings and three benchmark datasets. Experimental
results show that our models achieve overall better attacking performance and
produce more fluent and grammatical adversarial samples compared to baseline
models. Besides, our adversarial attack agent significantly reduces the query
times in both attack settings. Our codes are released at
https://github.com/Yangyi-Chen/MAYA.
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