Papers › ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

20 Nov 2020EMNLP 2021 11arXiv:2011.10369archive 2025-07-28

Fanchao Qi, Yangyi Chen, Mukai Li, Yuan YAO, Zhiyuan Liu, Maosong Sun

Backdoor attacks are a kind of emergent training-time threat to deep neural networks (DNNs). They can manipulate the output of DNNs and possess high insidiousness. In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models. Nevertheless, there are few studies on defending against textual backdoor attacks. In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of our knowledge, is the first method that can handle all the textual backdoor attack situations. Experiments demonstrate the effectiveness of our model in defending BiLSTM and BERT against five different backdoor attacks. All the code and data of this paper can be obtained at https://github.com/thunlp/ONION.

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3ran · our draft was wrong
1unverified

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filter_sent thunlp/ONION/experiments/test_defense.py official repository ran · our draft was wrong MIT (permissive) · 0a3034959f19105b · report
get_all_data thunlp/ONION/experiments/run_poison_bert.py official repository ran · our draft was wrong MIT (permissive) · 0762630368cd3261 · report
read_data thunlp/ONION/experiments/run_poison_bert.py official repository ran · our draft was wrong MIT (permissive) · 213b3de0b016d2f7 · report
evaluaion thunlp/ONION/experiments/run_poison_bert.py official repository unverified MIT (permissive) · 31ff894ab3f7a745 · report

Tasks

Backdoor Attackbackdoor defense

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Methods

AdamAttentionAttention DropoutBERTBiLSTMDense ConnectionsDropoutLSTMLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSigmoid ActivationSoftmaxTanh ActivationWeight DecayWordPiece

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