Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, pp. 1735–1780, 1997
1997
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R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Conference on Empirical Methods in Natural Language Processing , 2013
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR , vol. abs/1412.6980, 2014
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2014
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X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” ArXiv , vol. abs/1509.01626, 2015
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2015
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T. Gu, B. Dolan-Gavitt, and S. Garg, “BadNets: Identifying vulnerabilities in the machine learning model supply chain,” ArXiv , vol. abs/1708.06733, 2017
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2017
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J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 328–339. [Online]. Available: https://aclanthology.org/P18-1031
2018
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M. Iyyer, J. Wieting, K. Gimpel, and L. Zettlemoyer, “Adversarial example generation with syntactically controlled paraphrase networks,” in North American Chapter of the Association for Computational Linguistics , 2018
2018
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Q. Wang, B. Li, T. Xiao, J. Zhu, C. Li, D. F. Wong, and L. S. Chao, “Learning deep transformer models for machine translation,” in Annual Meeting of the Association for Computational Linguistics , 2019
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Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Neural Information Processing Systems , 2019
2019
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Similar

J. Dai, C. Chen, and Y. Li, “A backdoor attack against LSTM-based text classification systems,” IEEE Access , vol. 7, pp. 138 872–138 878, 2019
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” ArXiv , vol. abs/1810.04805, 2019
Original
2019
Cited alongside, same era.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A lite BERT for self-supervised learning of language representations,” ArXiv , vol. abs/1909.11942, 2019
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2019
Cited alongside, same era.
V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,” ArXiv , vol. abs/1910.01108, 2019

Then

X. Chen, A. Salem, D. Chen, M. Backes, S. Ma, Q. Shen, Z. Wu, and Y. Zhang, “BadNL: Backdoor attacks against NLP models with semantic-preserving improvements,” Annual Computer Security Applications Conference , 2020
2020
Later among the works it cites.
K. Krishna, J. Wieting, and M. Iyyer, “Reformulating unsupervised style transfer as paraphrase generation,” ArXiv , vol. abs/2010.05700, 2020
Original
2020
Later among the works it cites.
K. Chen, Y. Meng, X. Sun, S. Guo, T. Zhang, J. Li, and C. Fan, “Badpre: Task-agnostic backdoor attacks to pre-trained NLP foundation models,” CoRR , vol. abs/2110.02467, 2021. [Online]. Available: https://arxiv.org/abs/2110.02467
Original
2021
Later among the works it cites.
F. Qi, Y. Chen, M. Li, Y. Yao, Z. Liu, and M. Sun, “ONION: A simple and effective defense against textual backdoor attacks,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , M.-F. Moens, X. Huang, L. Specia, and S. W.-t. Yih, Eds. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 9558–9566. [Online]. Available: https://aclanthology.org/2021.emnlp-main.752

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2024

OrderBkd: Textual backdoor attack through repositioning

Alekseevskaia, Irina, Arkhipenko, Konstantin

Understand

The use of third-party datasets and pre-trained machine learning models poses a threat to NLP systems due to possibility of hidden backdoor attacks.

Existing attacks involve poisoning the data samples such as insertion of tokens or sentence paraphrasing, which either alter the semantics of the original texts or can be detected.
Our main difference from the previous work is that we use the reposition of a two words in a sentence as a trigger.
By designing and applying specific part-of-speech (POS) based rules for selecting these tokens, we maintain high attack success rate on SST-2 and AG classification datasets while outperforming existing attacks in terms of perplexity and semantic similarity to the clean samples.

Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Conference on Empirical Methods in Natural Language Processing , 2013
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR , vol. abs/1412.6980, 2014
Original
2014
Earlier work this paper cites.
X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” ArXiv , vol. abs/1509.01626, 2015
Original
2015
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “BadNets: Identifying vulnerabilities in the machine learning model supply chain,” ArXiv , vol. abs/1708.06733, 2017
Original
2017
Earlier work this paper cites.
J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 328–339. [Online]. Available: https://aclanthology.org/P18-1031
2018
Earlier work this paper cites.
M. Iyyer, J. Wieting, K. Gimpel, and L. Zettlemoyer, “Adversarial example generation with syntactically controlled paraphrase networks,” in North American Chapter of the Association for Computational Linguistics , 2018
2018
Earlier work this paper cites.
Q. Wang, B. Li, T. Xiao, J. Zhu, C. Li, D. F. Wong, and L. S. Chao, “Learning deep transformer models for machine translation,” in Annual Meeting of the Association for Computational Linguistics , 2019
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Neural Information Processing Systems , 2019
2019
Earlier work this paper cites.

Similar

J. Dai, C. Chen, and Y. Li, “A backdoor attack against LSTM-based text classification systems,” IEEE Access , vol. 7, pp. 138 872–138 878, 2019
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” ArXiv , vol. abs/1810.04805, 2019
Original
2019
Cited alongside, same era.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A lite BERT for self-supervised learning of language representations,” ArXiv , vol. abs/1909.11942, 2019
Original
2019
Cited alongside, same era.
V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,” ArXiv , vol. abs/1910.01108, 2019

Then

X. Chen, A. Salem, D. Chen, M. Backes, S. Ma, Q. Shen, Z. Wu, and Y. Zhang, “BadNL: Backdoor attacks against NLP models with semantic-preserving improvements,” Annual Computer Security Applications Conference , 2020
2020
Later among the works it cites.
K. Krishna, J. Wieting, and M. Iyyer, “Reformulating unsupervised style transfer as paraphrase generation,” ArXiv , vol. abs/2010.05700, 2020
Original
2020
Later among the works it cites.
K. Chen, Y. Meng, X. Sun, S. Guo, T. Zhang, J. Li, and C. Fan, “Badpre: Task-agnostic backdoor attacks to pre-trained NLP foundation models,” CoRR , vol. abs/2110.02467, 2021. [Online]. Available: https://arxiv.org/abs/2110.02467
Original
2021
Later among the works it cites.
F. Qi, Y. Chen, M. Li, Y. Yao, Z. Liu, and M. Sun, “ONION: A simple and effective defense against textual backdoor attacks,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , M.-F. Moens, X. Huang, L. Specia, and S. W.-t. Yih, Eds. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 9558–9566. [Online]. Available: https://aclanthology.org/2021.emnlp-main.752

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2019

Cited alongside, same era.

J. Li, A. Sun, R. Han, and C. Li, “A survey on deep learning for named entity recognition,” IEEE Transactions on Knowledge and Data Engineering , vol. PP, pp. 1–1, 03 2020

2020

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Y. Goh, X. Cai, W. Theseira, G. Ko, and K. Khor, “Evaluating human versus machine learning performance in classifying research abstracts,” Scientometrics , vol. 125, 07 2020

2020

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W. E. Zhang, Q. Sheng, A. Alhazmi, and C. Li, “Adversarial attacks on deep-learning models in natural language processing: A survey,” ACM Transactions on Intelligent Systems and Technology , vol. 11, pp. 1–41, 04 2020

2020

Cited alongside, same era.

N. Carlini, F. Tramèr, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. X. Song, Ú. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,” in USENIX Security Symposium , 2020

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Cited alongside, same era.

2021

Later among the works it cites.

F. Qi, M. Li, Y. Chen, Z. Zhang, Z. Liu, Y. Wang, and M. Sun, “Hidden killer: Invisible textual backdoor attacks with syntactic trigger,” ArXiv , vol. abs/2105.12400, 2021

Original

2021

Later among the works it cites.

F. Qi, Y. Chen, X. Zhang, M. Li, Z. Liu, and M. Sun, “Mind the style of text! adversarial and backdoor attacks based on text style transfer,” ArXiv , vol. abs/2110.07139, 2021

Original

2021

Later among the works it cites.

G. Cui, L. Yuan, B. He, Y. Chen, Z. Liu, and M. Sun, “A unified evaluation of textual backdoor learning: Frameworks and benchmarks,” ArXiv , vol. abs/2206.08514, 2022

Original

2022

Later among the works it cites.

D. Cer, Y. Yang, S.-y. Kong, N. Hua, N. Limtiaco, R. St. John, N. Constant, M. Guajardo-Cespedes, S. Yuan, C. Tar, B. Strope, and R. Kurzweil, “Universal sentence encoder for English,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Brussels, Belgium: Association for Computational Linguistics, Nov. 2018, pp. 169–174. [Online]. Available: https://aclanthology.org/D18-2029

2029

Closest in time.