The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Generating Natural Language Adversarial Examples
Open accessAbstract
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.In the image domain, these perturbations are often virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree.However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document.Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively.We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar.Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples.We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.
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Glove: Global Vectors for Word Representation
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Explaining and Harnessing Adversarial Examples
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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
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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
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
Awadid, A., & Robert, B. (2025) · OASIcs : OpenAccess Series in Informatics · doi:10.4230/oasics.saia.2024.1 · Cited by 4,702
Learning Word Vectors for Sentiment Analysis
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Practical Black-Box Attacks against Machine Learning
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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377
Adversarial machine learning at scale
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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
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Li, L., Ma, R., Guo, Q., Xue, X., & Qiu, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.500 · Cited by 574
Recent Advances in Adversarial Training for Adversarial Robustness
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · arXiv · doi:10.48550/arxiv.2102.01356 · Cited by 490
BAE: BERT-based Adversarial Examples for Text Classification
Garg, S., & Ramakrishnan, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.498 · Cited by 478
Can Large Language Models Be an Alternative to Human Evaluations?
Chiang, C.-H., & Lee, H.-Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.870 · Cited by 331
Metadata
- Authors
- Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., & Chang, K.-W.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 2890-2896
- OpenAlex
- W2962718684
- Cited by
- 787
- References
- 27
- FWCI
- 35.47
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., & Chang, K.-W. (2018). Generating Natural Language Adversarial Examples. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d18-1316
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