VQUNet: Vector Quantization U-Net for Defending Adversarial Attacks by Regularizing Unwanted Noise

Author:

He Zhixun1ORCID,Singhal Mukesh1ORCID

Affiliation:

1. Electrical Engineering and Computer Science, University of California, Merced, United States

Publisher

ACM

Reference53 articles.

1. Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey

2. Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018).

3. Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow. 2018. Thermometer encoding: One hot way to resist adversarial examples. In International Conference on Learning Representations.

4. Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill. 2018. Ground-truth adversarial examples. openreview.net (2018).

5. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). IEEE, 39–57.

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