Random Walks for Adversarial Meshes

Author:

Belder Amir1,Yefet Gal1,Ben-Itzhak Ran1,Tal Ayellet1

Affiliation:

1. Technion - Israel Institute of Technology, Israel

Funder

Israel Science Foundation

Publisher

ACM

Reference40 articles.

1. Maksym Andriushchenko and Nicolas Flammarion . 2020. Understanding and Improving Fast Adversarial Training . In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates , Inc ., 16048–16059. https://proceedings.neurips.cc/paper/ 2020 /file/b8ce47761ed7b3b6f48b583350b7f9e4-Paper.pdf Maksym Andriushchenko and Nicolas Flammarion. 2020. Understanding and Improving Fast Adversarial Training. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates, Inc., 16048–16059. https://proceedings.neurips.cc/paper/2020/file/b8ce47761ed7b3b6f48b583350b7f9e4-Paper.pdf

2. AttWalk: Attentive Cross-Walks for Deep Mesh Analysis

3. Evasion Attacks against Machine Learning at Test Time

4. Towards Evaluating the Robustness of Neural Networks

5. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SAGA: Spectral Adversarial Geometric Attack on 3D Meshes;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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