GCMA: Generative Cross-Modal Transferable Adversarial Attacks from Images to Videos-Reference-Cited by-同舟云学术

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GCMA: Generative Cross-Modal Transferable Adversarial Attacks from Images to Videos

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

Chen Kai¹^ORCID,Wei Zhipeng¹^ORCID,Chen Jingjing¹^ORCID,Wu Zuxuan¹^ORCID,Jiang Yu-Gang¹^ORCID

Affiliation:

1. Fudan University, Shanghai, China

Funder

National Key R&D Program of China

NSFC project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612110

Reference43 articles.

1. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

2. Zero-Shot Ingredient Recognition by Multi-Relational Graph Convolutional Network

3. A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition

4. Attacking Video Recognition Models with Bullet-Screen Comments

5. GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement

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. Generative Imperceptible Attack With Feature Learning Bias Reduction and Multi-Scale Variance Regularization;IEEE Transactions on Information Forensics and Security;2024

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