Minority Reports Defense: Defending Against Adversarial Patches

Author:

McCoyd MichaelORCID,Park WonORCID,Chen Steven,Shah Neil,Roggenkemper Ryan,Hwang MinjuneORCID,Liu Jason XinyuORCID,Wagner David

Publisher

Springer International Publishing

Reference22 articles.

1. Brown, T., Mane, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch (2017). arXiv:1712.09665

2. Chiang, P.-y., Ni, R., Abdelkader, A., Zhu, C., Studor, C., Goldstein, T.: Certified defenses for adversarial patches. In: ICLR (2020)

3. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: Security and Privacy (2017). arXiv:1608.04644 [cs.CR]

4. Deotte, C.: How to choose CNN Architecture MNIST (2018). https://www.kaggle.com/cdeotte/how-to-choose-cnn-architecture-mnist

5. Devries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout (2017). arXiv:1708.04552 [cs.CV]

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