Exploiting natural language services: a polarity based black-box attack

Graphical abstract

References

[1] Gao J, Lanchantin J, Soffa M L, Qi Y. Black-box generation of adversarial text sequences to evade deep learning classifiers. In: Proceedings of 2018 IEEE Security and Privacy Workshops (SPW). 2018, 50– 56

[2] Vijayaraghavan P, Roy D. Generating black-box adversarial examples for text classifiers using a deep reinforced model. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2019, 711– 726

[3] Alzantot M, Sharma Y, Elgohary A, Ho B J, Srivastava M, Chang K W. Generating natural language adversarial examples. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 2890−2896

[4] Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. Advances in neural information processing systems, 2015, 649– 657

[5] Maas A L, Daly R E, Pham P T, Huang D, Ng A Y, Potts C. Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1. 2011, 142– 150

Acknowledgements

The authors wish to thank the members of NOVA Research Lab at Yildiz Technical University, Turkey for their valuable suggestions.

Supporting Information

The supporting information is available online at journal.hep.com.cn and link.springer.com.

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