AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning-Reference-Cited by-同舟云学术

AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Zhou Ziqi¹^ORCID,Hu Shengshan¹^ORCID,Li Minghui¹^ORCID,Zhang Hangtao¹^ORCID,Zhang Yechao¹^ORCID,Jin Hai¹^ORCID

Affiliation:

1. Huazhong University of Science and Technology, Wuhan, China

Funder

Hubei Province Key Research and Development Technology Special Innovation Project

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612454

Reference59 articles.

1. Yuanhao Ban and Yinpeng Dong . 2022 . Pre-trained Adversarial Perturbations . In Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS'22) . Yuanhao Ban and Yinpeng Dong. 2022. Pre-trained Adversarial Perturbations. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS'22).

2. Universal Adversarial Attacks on Text Classifiers

3. Tom B. Brown , Dandelion Mané , Aurko Roy , Martín Abadi , and Justin Gilmer . 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 ( 2017 ). Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 (2017).

4. Nicholas Carlini and David Wagner . 2016. Defensive distillation is not robust to adversarial examples. arXiv preprint arXiv:1607.04311 ( 2016 ). Nicholas Carlini and David Wagner. 2016. Defensive distillation is not robust to adversarial examples. arXiv preprint arXiv:1607.04311 (2016).

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