The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Open accessAbstract
Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods.Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency.In this paper, we propose BERT-Attack, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT.We turn BERT against its fine-tuned models and other deep neural models in downstream tasks so that we can successfully mislead the target models to predict incorrectly.Our method outperforms state-of-theart attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved.Also, the cost of calculation is low, thus possible for large-scale generations.The code is available at https://github.com/ LinyangLee/BERT-Attack.
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Self-citationPre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994
Self-citationAuditing large language models: a three-layered approach
Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2024) · AI and Ethics · doi:10.1007/s43681-023-00289-2 · Cited by 289
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Metadata
- Authors
- Li, L., Ma, R., Guo, Q., Xue, X., & Qiu, X.
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 6193-6202
- OpenAlex
- W3101449015
- Cited by
- 574
- References
- 23
- FWCI
- 35.28
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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