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BAE: BERT-based Adversarial Examples for Text Classification

2020-04-04 16:25:48
Siddhant Garg, Goutham Ramakrishnan
arXiv_CL
           
Abstract

Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans but which get misclassified by the model. We present BAE, a powerful black box attack for generating grammatically correct and semantically coherent adversarial examples. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging a language model to generate alternatives for the masked tokens. Compared to prior work, we show that BAE performs a stronger attack on three widely used models for seven text classification datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2004.01970

PDF

https://arxiv.org/pdf/2004.01970.pdf

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