Visual Prompting for Adversarial Robustness-Reference-Cited by-同舟云学术

Visual Prompting for Adversarial Robustness

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Chen Aochuan¹,Lorenz Peter²,Yao Yuguang¹,Chen Pin-Yu³,Liu Sijia¹

Affiliation:

1. Michigan State University,USA

2. Fraunhofer ITWM and Fraunhofer Center of Machine Learning,Germany

3. IBM Research,USA

Publisher

IEEE

Link

Reference46 articles.

1. Evaluating the adversarial robustness of adaptive test-time defenses;croce;arXiv 2202 13711,2022

2. On detecting adversarial perturbations;metzen;arXiv 1702 04267,2017

3. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks;croce;ICML,2020

4. Ml-loo: Detecting adversarial examples with feature attribution;yang;arXiv 1906 03499,2019

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2. Adversarial Examples are Misaligned in Diffusion Model Manifolds;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Exploring the Benefits of Visual Prompting in Differential Privacy;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01