References

Carlini, N., & Wagner, D. (2017). Towards Evaluating the Robustness of Neural Networks. ArXiv:1608.04644 [Cs]. http://arxiv.org/abs/1608.04644

Cisse, M., Adi, Y., Neverova, N., & Keshet, J. (2017). Houdini: Fooling Deep Structured Prediction Models. ArXiv:1707.05373 [Cs, Stat]. http://arxiv.org/abs/1707.05373

Dubey, A., Cammarota, R., & Aysu, A. (2019). MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection. ArXiv:1910.13063 [Cs]. http://arxiv.org/abs/1910.13063

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018). Robust Physical-World Attacks on Deep Learning Models. ArXiv:1707.08945 [Cs]. http://arxiv.org/abs/1707.08945

Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut Learning in Deep Neural Networks. ArXiv:2004.07780 [Cs, q-Bio]. http://arxiv.org/abs/2004.07780

Goodfellow, I., Papernot, N., Huang, S., Duan, R., Abeel, P., & Clark, J. (2017). Attacking Machine Learning with Adversarial Examples. https://openai.com/blog/adversarial-example-research/

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat].

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. ArXiv:1512.03385 [Cs]. http://arxiv.org/abs/1512.03385

Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. ArXiv:1503.02531 [Cs, Stat]. http://arxiv.org/abs/1503.02531

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (n.d.). Adversarial Machine Learning. 15.

Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., & Madry, A. (2019). Adversarial Examples Are Not Bugs, They Are Features. ArXiv:1905.02175 [Cs, Stat]. http://arxiv.org/abs/1905.02175

Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial examples in the physical world. ArXiv:1607.02533 [Cs, Stat].

Lu, J., Issaranon, T., & Forsyth, D. (2017). SafetyNet: Detecting and Rejecting Adversarial Examples Robustly. ArXiv:1704.00103 [Cs]. http://arxiv.org/abs/1704.00103

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards Deep Learning Models Resistant to Adversarial Attacks. ArXiv:1706.06083 [Cs, Stat]. http://arxiv.org/abs/1706.06083

Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016). DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.282

Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017). Practical Black-Box Attacks against Machine Learning. ArXiv:1602.02697 [Cs]. http://arxiv.org/abs/1602.02697

Papernot, N., Faghri, F., Carlini, N., Goodfellow, I., Feinman, R., Kurakin, A., Xie, C., Sharma, Y., Brown, T., Roy, A., Matyasko, A., Behzadan, V., Hambardzumyan, K., Zhang, Z., Juang, Y.-L., Li, Z., Sheatsley, R., Garg, A., Uesato, J., … McDaniel, P. (2018). Technical Report on the CleverHans v2.1.0 Adversarial Examples Library. ArXiv:1610.00768 [Cs, Stat]. http://arxiv.org/abs/1610.00768

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2015). The Limitations of Deep Learning in Adversarial Settings. ArXiv:1511.07528 [Cs, Stat]. http://arxiv.org/abs/1511.07528

Papernot, N., McDaniel, P., & Goodfellow, I. (2016). Transferability in Machine Learning: From Phenomena to Black-Box Attacks using Adversarial Samples. ArXiv:1605.07277 [Cs]. http://arxiv.org/abs/1605.07277

Qin, Y., Carlini, N., Goodfellow, I., Cottrell, G., & Raffel, C. (n.d.). Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. 12.

Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. ArXiv:1711.05225 [Cs, Stat]. http://arxiv.org/abs/1711.05225

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3), 211–252. https://doi.org/10.1007/s11263-015-0816-y

Su, J., Vargas, D. V., & Kouichi, S. (2019). One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation, 23(5), 828–841. https://doi.org/10.1109/TEVC.2019.2890858

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. ArXiv:1312.6199 [Cs].

Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2015.7298594

Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., & Ma, A. (2019). Robustness May Be at Odds with Accuracy. 23.

Xiao, C., Deng, R., Li, B., Yu, F., Liu, M., & Song, D. (2018). Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation. ArXiv:1810.05162 [Cs]. http://arxiv.org/abs/1810.05162

Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., & Song, D. (2019). Generating Adversarial Examples with Adversarial Networks. ArXiv:1801.02610 [Cs, Stat]. http://arxiv.org/abs/1801.02610

Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., & Yuille, A. (2017). Adversarial Examples for Semantic Segmentation and Object Detection. ArXiv:1703.08603 [Cs]. http://arxiv.org/abs/1703.08603

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