The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
HotFlip: White-Box Adversarial Examples for Text Classification
Open accessAbstract
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.We find that only a few manipulations are needed to greatly decrease the accuracy.Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the onehot input vectors.Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time.With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.
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Cites
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Towards Evaluating the Robustness of Neural Networks
Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602
The Limitations of Deep Learning in Adversarial Settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993
Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv · doi:10.48550/arxiv.1508.06615 · Cited by 1,530
Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R., & Liang, P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1215 · Cited by 1,410
Very Deep Convolutional Networks for Text Classification
Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1104 · Cited by 1,046
Generating Natural Adversarial Examples
Zhao, Z., Dua, D., & Singh, S. (2017) · arXiv · doi:10.48550/arxiv.1710.11342 · Cited by 508
Cited by
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
How Can We Know What Language Models Know?
Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00324 · Cited by 1,002
Generating Natural Language Adversarial Examples
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1316 · Cited by 787
Universal Adversarial Triggers for Attacking and Analyzing NLP
Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1221 · Cited by 642
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Iyyer, M., Wieting, J., Gimpel, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1170 · Cited by 615
Metadata
- Authors
- Ebrahimi, J., Rao, A., Lowd, D., & Dou, D.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2018
- Pages
- 31-36
- OpenAlex
- W2799194071
- Cited by
- 922
- References
- 9
- FWCI
- 39.84
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018). HotFlip: White-Box Adversarial Examples for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.18653/v1/p18-2006
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
