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

Fatma GUMUS; M. Fatih AMASYALI

doi:10.1007/s11704-021-0198-y

Front. Comput. Sci. ›› 2022, Vol. 16 ›› Issue (5) : 165325 DOI: 10.1007/s11704-021-0198-y

Artificial Intelligence

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

Fatma GUMUS ¹^,²^,^†
, M. Fatih AMASYALI ¹

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Fatma GUMUS, M. Fatih AMASYALI. Exploiting natural language services: a polarity based black-box attack. Front. Comput. Sci., 2022, 16(5): 165325 DOI:10.1007/s11704-021-0198-y

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References

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[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