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The vulnerability to adversarial attacks has been a critical issue for deep neural networks.
Learning question classifiers
Li, X. and Roth, D · 2002
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M. K · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Maximum resilience of artificial neural networks
Cheng, C.-H., Nührenberg, G., and Ruess, H · 2017
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Houdini: Fooling deep structured prediction models
Cissé, M., Adi, Y., Neverova, N., and Keshet, J · 2017
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Crafting adversarial examples for speech paralinguistics applications
Gong, Y. and Poellabauer, C · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P · 2017
Cited alongside, same era.
Reluplex: An efficient smt solver for verifying deep neural networks
Katz, G., Barrett, C., Dill, D. L., Julian, K., and Kochenderfer, M. J · 2017
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
Cited alongside, same era.
Audio adversarial examples: Targeted attacks on speech-to-text
Carlini, N. and Wagner, D. A · 2018
Cited alongside, same era.
Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
Later among the works it cites.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Gao, J., Lanchantin, J., Soffa, M. L., and Qi, Y · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
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Fast and effective robustness certification
Singh, G., Gehr, T., Mirman, M., Püschel, M., and Vechev, M · 2018
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Efficient neural network robustness certification with general activation functions
Zhang, H., Weng, T.-W., Chen, P.-Y., Hsieh, C.-J., and Daniel, L · 2018
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Generating natural adversarial examples
Zhao, Z., Dua, D., and Singh, S · 2018
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Cheng, M., Yi, J., Zhang, H., Chen, P., and Hsieh, C · 2018
Cited alongside, same era.
A dual approach to scalable verification of deep networks
Dvijotham, K., Stanforth, R., Gowal, S., Mann, T., and Kohli, P · 2018
Cited alongside, same era.
On adversarial examples for character-level neural machine translation
Ebrahimi, J., Lowd, D., and Dou, D
Cited in the paper.
Hotflip: White-box adversarial examples for text classification
Ebrahimi, J., Rao, A., Lowd, D., and Dou, D
Cited in the paper.
Crafting adversarial input sequences for recurrent neural networks
Papernot, N., McDaniel, P., Swami, A., and Harang, R
Cited in the paper.
Crafting adversarial input sequences for recurrent neural networks
Papernot, N., McDaniel, P. D., Swami, A., and Harang, R. E
Cited in the paper.
Towards fast computation of certified robustness for relu networks
Weng, T.-W., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Boning, D., Dhillon, I. S., and Daniel, L
Cited in the paper.
Later among the works it cites.
Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks
Boopathy, A., Weng, T.-W., Chen, P.-Y., Liu, S., and Daniel, L · 2019
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