Built on

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1765–1773
2017
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” arXiv preprint arXiv:1708.06733 , 2017
Original
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017
Original
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
A. Katharopoulos and F. Fleuret, “Not all samples are created equal: Deep learning with importance sampling,” in International conference on machine learning . PMLR, 2018, pp. 2525–2534
2018
Earlier work this paper cites.
C. Sun, X. Qiu, Y. Xu, and X. Huang, “How to fine-tune bert for text classification?” in Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings 18 . Springer, 2019, pp. 194–206
2019
Earlier work this paper cites.
E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh, “Universal adversarial triggers for attacking and analyzing nlp,” arXiv preprint arXiv:1908.07125 , 2019
Original
2019
Earlier work this paper cites.
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
Earlier work this paper cites.

Similar

Y. Liu, “Fine-tune bert for extractive summarization,” arXiv preprint arXiv:1903.10318 , 2019
Original
2019
Cited alongside, same era.
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang, “Pre-trained models for natural language processing: A survey,” Science China Technological Sciences , vol. 63, no. 10, pp. 1872–1897, 2020
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE , vol. 109, no. 1, pp. 43–76, 2020
2020
Cited alongside, same era.

Then

Z. Li, P. Xia, R. Tao, H. Niu, and B. Li, “A new perspective on stabilizing gans training: Direct adversarial training,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 7, no. 1, pp. 178–189, 2022
2022
Later among the works it cites.
P. Xia, Z. Li, W. Zhang, and B. Li, “Data-efficient backdoor attacks,” arXiv preprint arXiv:2204.12281 , 2022
Original
2022
Later among the works it cites.
P. Xia, H. Niu, Z. Li, and B. Li, “Enhancing backdoor attacks with multi-level mmd regularization,” IEEE Transactions on Dependable and Secure Computing , vol. 20, no. 2, pp. 1675–1686, 2022
2022
Later among the works it cites.
X. Chen, Y. Dong, Z. Sun, S. Zhai, Q. Shen, and Z. Wu, “Kallima: A clean-label framework for textual backdoor attacks,” in Computer Security–ESORICS 2022: 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, September 26–30, 2022, Proceedings, Part I . Springer, 2022, pp. 447–466
2022

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

Efficient Trigger Word Insertion

Zeng, Yueqi, Li, Ziqiang, Xia, Pengfei et al.

Understand

With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models.

However, previous works hardly consider the effect of the poisoning rate.
In this paper, our main objective is to reduce the number of poisoned samples while still achieving a satisfactory Attack Success Rate (ASR) in text backdoor attacks.
To accomplish this, we propose an efficient trigger word insertion strategy in terms of trigger word optimization and poisoned sample selection.

Built on

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1765–1773
2017
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” arXiv preprint arXiv:1708.06733 , 2017
Original
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017
Original
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
A. Katharopoulos and F. Fleuret, “Not all samples are created equal: Deep learning with importance sampling,” in International conference on machine learning . PMLR, 2018, pp. 2525–2534
2018
Earlier work this paper cites.
C. Sun, X. Qiu, Y. Xu, and X. Huang, “How to fine-tune bert for text classification?” in Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings 18 . Springer, 2019, pp. 194–206
2019
Earlier work this paper cites.
E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh, “Universal adversarial triggers for attacking and analyzing nlp,” arXiv preprint arXiv:1908.07125 , 2019
Original
2019
Earlier work this paper cites.
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
Earlier work this paper cites.

Similar

Y. Liu, “Fine-tune bert for extractive summarization,” arXiv preprint arXiv:1903.10318 , 2019
Original
2019
Cited alongside, same era.
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang, “Pre-trained models for natural language processing: A survey,” Science China Technological Sciences , vol. 63, no. 10, pp. 1872–1897, 2020
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE , vol. 109, no. 1, pp. 43–76, 2020
2020
Cited alongside, same era.

Then

Z. Li, P. Xia, R. Tao, H. Niu, and B. Li, “A new perspective on stabilizing gans training: Direct adversarial training,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 7, no. 1, pp. 178–189, 2022
2022
Later among the works it cites.
P. Xia, Z. Li, W. Zhang, and B. Li, “Data-efficient backdoor attacks,” arXiv preprint arXiv:2204.12281 , 2022
Original
2022
Later among the works it cites.
P. Xia, H. Niu, Z. Li, and B. Li, “Enhancing backdoor attacks with multi-level mmd regularization,” IEEE Transactions on Dependable and Secure Computing , vol. 20, no. 2, pp. 1675–1686, 2022
2022
Later among the works it cites.
X. Chen, Y. Dong, Z. Sun, S. Zhai, Q. Shen, and Z. Wu, “Kallima: A clean-label framework for textual backdoor attacks,” in Computer Security–ESORICS 2022: 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, September 26–30, 2022, Proceedings, Part I . Springer, 2022, pp. 447–466
2022

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

K. Kurita, P. Michel, and G. Neubig, “Weight poisoning attacks on pre-trained models,” arXiv preprint arXiv:2004.06660 , 2020

Original

2020

Cited alongside, same era.

H. Zhong, C. Liao, A. C. Squicciarini, S. Zhu, and D. Miller, “Backdoor embedding in convolutional neural network models via invisible perturbation,” in Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy , 2020, pp. 97–108

2020

Cited alongside, same era.

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 preprint arXiv:2110.07139 , 2021

Original

2021

Cited alongside, same era.

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,” in Annual Computer Security Applications Conference , 2021, pp. 554–569

2021

Cited alongside, same era.

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 preprint arXiv:2105.12400 , 2021

Original

2021

Cited alongside, same era.

F. Qi, Y. Yao, S. Xu, Z. Liu, and M. Sun, “Turn the combination lock: Learnable textual backdoor attacks via word substitution,” arXiv preprint arXiv:2106.06361 , 2021

Original

2021

Cited alongside, same era.

W. Yang, L. Li, Z. Zhang, X. Ren, X. Sun, and B. He, “Be careful about poisoned word embeddings: Exploring the vulnerability of the embedding layers in nlp models,” arXiv preprint arXiv:2103.15543 , 2021

Original

2021

Cited alongside, same era.

X. Zhang, Z. Zhang, S. Ji, and T. Wang, “Trojaning language models for fun and profit,” in 2021 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2021, pp. 179–197

2021

Cited alongside, same era.

Later among the works it cites.

Z. Li, M. Usman, R. Tao, P. Xia, C. Wang, H. Chen, and B. Li, “A systematic survey of regularization and normalization in gans,” ACM Computing Surveys , vol. 55, no. 11, pp. 1–37, 2023

2023

Closest in time.

Z. Li, P. Xia, H. Sun, Y. Zeng, W. Zhang, and B. Li, “Explore the effect of data selection on poison efficiency in backdoor attacks,” arXiv preprint arXiv:2310.09744 , 2023

Original

2023

Closest in time.

Z. Li, H. Sun, P. Xia, B. Xia, X. Rui, W. Zhang, and B. Li, “A proxy-free strategy for practically improving the poisoning efficiency in backdoor attacks,” arXiv preprint arXiv:2306.08313 , 2023

Original

2023

Closest in time.

P. Xia, Y. Zeng, Z. Li, W. Zhang, and B. Li, “Efficient trojan injection: 90% attack success rate using 0.04% poisoned samples,” 2023. [Online]. Available: https://openreview.net/forum?id=ogsUO9JHZu0

2023

Closest in time.

S. Zhuang, P. Xia, and B. Li, “An empirical study of backdoor attacks on masked auto encoders,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

2023

Closest in time.

H. Sun, Z. Li, P. Xia, H. Li, B. Xia, Y. Wu, and B. Li, “Efficient backdoor attacks for deep neural networks in real-world scenarios,” arXiv preprint arXiv:2306.08386 , 2023

Original

2023

Closest in time.

J. Yan, V. Gupta, and X. Ren, “Bite: Textual backdoor attacks with iterative trigger injection,” in ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning , 2023

2023

Closest in time.