On the Robustness of Vision Transformers to Adversarial Examples-Reference-Cited by-同舟云学术

On the Robustness of Vision Transformers to Adversarial Examples

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Mahmood Kaleel¹,Mahmood Rigel¹,van Dijk Marten²

Affiliation:

1. University of Connecticut,Department of Computer Science and Engineering,CT,USA,06269

2. CWI,Amsterdam,The Netherlands

Publisher

IEEE

Link

Reference38 articles.

1. Attacks which do not kill training make adversarial learning stronger;zhang;International Conference on Machine Learning,2020

2. Muldef:Multi-model-based defense against adversarial examples for neural networks;srisakaokul;arXiv preprint arXiv 1809 00065,2018

3. Robustness verification for transformers;shi;ICLR 2020,0

4. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples;papernot;CoRR,2016

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