A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense-Reference-Cited by-同舟云学术

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A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense

Published:2024 Issue: Volume:12 Page:69206-69216
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Iijima Ryota¹^ORCID,Shiota Sayaka¹^ORCID,Kiya Hitoshi¹^ORCID

Affiliation:

1. Department of Computer Science, Tokyo Metropolitan University, Tokyo, Japan

Funder

Japan Society for the Promotion of Science (JSPS) KAKENHI

Japan Science and Technology Agency (JST) Core Research for Evolutionary Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10530249.pdf?arnumber=10530249

Reference73 articles.

1. Explaining and harnessing adversarial examples;Goodfellow;arXiv:1412.6572,2014

2. Adversarial machine learning at scale;Kurakin

3. Theoretically principled trade-off between robustness and accuracy;Zhang

4. Unlabeled data improves adversarial robustness;Carmon,2019

5. Encryption Inspired Adversarial Defense For Visual Classification

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. Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-Trained Models;IEEE Open Journal of Signal Processing;2024

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