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Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

27 Jul 2019arXiv:1907.11932archive 2025-07-28

Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective---it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving---it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient---it generates adversarial text with computational complexity linear to the text length. *The code, pre-trained target models, and test examples are available at https://github.com/jind11/TextFooler.

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Code

Syntology Ran 2 of 2 code samples harvested from 0 repositories linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran · fixture could not drive it.

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jind11/TextFooler officialmentioned in papermentioned on GitHubpytorch report
DAI-Lab/fibber mentioned on GitHubtfMIT report
Jason-J-Choi/DeBERTa_TxtClassifier mentioned on GitHubpytorch report
utsjiyaoli/qa-attack mentioned on GitHubpytorch report
wqj111186/TextFooler mentioned on GitHubpytorch report
yuehwai0508/Bert_TFL mentioned on GitHubpytorch report

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Code Syntology ran Syntology

2 samples harvested; 2 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
1ran · fixture could not drive it

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Tasks

Adversarial TextGeneral ClassificationNatural Language InferenceText Classification

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Methods

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPiece

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