|
C.Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhun, I. Goodfellow and R. Fergus, “Intriguing properties of neural networks”, 2nd International Conference on Learning Representations, ICLR 2014, Banff, Canada, 2014.
|
|
R. Jia., P. Liang, Adversarial examples for evaluating reading comprehension systems. In EMNLP, 2017
|
|
Y. Belinkov and Y. Bisk, “Synthetic and natural noise both break neural machine translation”, In Proceedings of ICLR, 2018.
|
|
I. Fursov, A. Zaytsev, P. Burnyshev, E. Dmitrieva, N. Klyuchnikov, A. Kravchenko, E. aArtemova and E. Burnaev, “A differentiable language model adversarial attack on text classifiers”, arXiv:2107.11275v1 [cs.CL], 23 Jul 2021.
|
|
Z. Kong, J. Xue, Y. Wang, L. Huang, Z. Niu and E. Li, “A survey on adversarial attack in the age of artificial intelligence”, Wireless Communications and Mobile Computing, Volume 2021, Article ID 4907754, 22 pages, 2021.
|
|
J. Xu and Q. Du, “TextTricker:Loss-based and gradient-based adversarial attacks on text classification models”, Engineering Applications of Artificial Intelligence,Volume 92, Elsevier, 0952-1976, 2020.
|
|
H. Hosseini, S. Kannan, B. Zhang and R. Poovendran, “Deceiving google’s perspective api built for detecting toxic comments,” arXiv preprint arXiv:1702.08138, 2017.
|
|
M. Alzantot, Y. Sharma, A. Elgohary, B. Ho, M. Srivastava and K. Chang, “Generating natural language adversarial examples, in Proceedings of Conference on Empiritical Methods in Natural Language Processing (EMNLP), 2018.
|
|
B. Liang, H. Li, M. Su, P. Bian, X. Li and W. ChangShi, “Deep text classification can be fooled”, arXiv preprint arXiv:1704.08006, 2017.
|
|
S. Samanta and S. Mehta, “Towards crafting text adversarial samples”, arXiv preprint arXiv:2003.10388, July 2017.
|
|
N. Papernot, P. McDaniel, A. Swami and R. Harang,“Crafting adversarial input sequences for recurrent neural networks”, In 2016 IEEE Military Communications Conference, MILCOM 2016, Baltimore, MD, USA, p.p. 49–54, November 1-3, 2016.
|
|
M. Sato, J. Suzuki, H. Shindo and Y. Matsumoto, “Interpretable adversarial perturbation in input embedding space for text”, In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, (IJCAI 2018), Stockholm, Sweden, p.p. 4323– 4330, July 13-19, 2018.
|
|
M. Behjati, S. M. Moosavi-Dezfooli, M. SoleymaniBaghshah and P. Frossard, “Universal adversarial attacks on text classifiers”, In ICASSP, 2019.
|
|
L. Song, X. Yu, H. Peng and K. Narasimhan, “Universal adversarial attacks with natural triggers for text classification”,
arXiv:2005.00174v2 [cs.CL], 7 Apr 2021.
|
|
S. Ren, Y. Deng, H. He and W. Che,“Generating natural language adversarial examples through probability weighted word saliency”, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, p.p. 1085–1097, 2019.
|
|
J. Ebrahimi, A. Rao, D. Lowd and D. Dou, “Hotflip: White-box adversarial examples for text classification”, In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Volume 2: Short Papers, p.p. 31–36, 2018.
|
|
E. Wallace, S Feng, N. Kandpal, M. Gardner and S. Singh, “Universal adversarial triggers for attacking and analyzing nlp”, arXiv preprint arXiv:1908.07125, 2019.
|
|
H. Zhang, H. Zhou, N. Miao and L. Li, “Generating fluent adversarial examples for natural languages”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019.
|
|
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D Manning, A. Ng and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank”, in Proceedings of the conference on empirical methods in natural language processing (EMNLP), p.p. 1631–1642, 2013.
|
|
T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch and A. Joulin, “Advances in pre-training distributed word representations”, In LREC, 2018.
|
|
