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Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training.
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples
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Towards Crafting Text Adversarial Samples
Samanta, S.; and Mehta, S. 2017 · 2017
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Generating Natural Language Adversarial Examples
Alzantot, M.; Sharma, Y.; Elgohary, A.; Ho, B.; Srivastava, M. B.; and Chang, K. 2018 · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
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Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Cheng, M.; Yi, J.; Chen, P.-Y.; Zhang, H.; and Hsieh, C.-J. 2018 · 2018
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HotFlip: White-Box adversarial examples for text classification
Ebrahimi, J.; Rao, A.; Lowd, D.; and Dou, D. 2018 · 2018
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Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Scalable Verified Training for Provably Robust Image Classification
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Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
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Certified robustness to adversarial word substitutions
Jia, R.; Raghunathan, A.; Göksel, K.; and Liang, P. 2019 · 2019
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TextBugger: Generating adversarial text against real-world applications
Li, J.; Ji, S.; Du, T.; Li, B.; and Wang, T. 2019 · 2019
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Countering adversarial images using input transformations
Guo, C.; Rana, M.; Cisse, M.; and van der Maaten, L. 2018 · 2018
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Iyyer, M.; Wieting, J.; Gimpel, K.; and Zettlemoyer, L. 2018 · 2018
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Kannan, H.; Kurakin, A.; and Goodfellow, I. J. 2018 · 2018
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Adversarial examples for natural language classification problems
Kuleshov, V.; Thakoor, S.; Lau, T.; and Ermon, S. 2018 · 2018
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Deep text classification can be fooled
Liang, B.; Li, H.; Su, M.; Bian, P.; Li, X.; and Shi, W. 2018 · 2018
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Semantically Equivalent Adversarial Rules for Debugging NLP models
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2018 · 2018
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Neekhara, P.; Hussain, S.; Dubnov, S.; and Koushanfar, F. 2019 · 2019
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Combating adversarial misspellings with robust word recognition
Pruthi, D.; Dhingra, B.; and Lipton, Z. C. 2019 · 2019
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Generating natural language adversarial examples through probability weighted word saliency
Ren, S.; Deng, Y.; He, K.; and Che, W. 2019 · 2019
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Improving the generalization of adversarial training with domain adaptation
Song, C.; He, K.; Wang, L.; and Hopcroft, J. E. 2019 · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E. P.; Ghaoui, L. E.; and Jordan, M. I. 2019 · 2019
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Learning to discriminate perturbations for blocking adversarial attacks in text classification
Zhou, Y.; Jiang, J.-Y.; Chang, K.-W.; and Wang, W. 2019 · 2019
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MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Ding, G. W.; Sharma, Y.; Lui, K. Y. C.; and Huang, R. 2020 · 2020
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Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Wang, Y.; Zou, D.; Yi, J.; Bailey, J.; Ma, X.; and Gu, Q. 2020 · 2020
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