Delving into Data: Effectively Substitute Training for Black-box Attack-Reference-Cited by-同舟云学术

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Delving into Data: Effectively Substitute Training for Black-box Attack

Published:2021-06 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Wang Wenxuan,Yin Bangjie,Yao Taiping,Zhang Li,Fu Yanwei,Ding Shouhong,Li Jilin,Huang Feiyue,Xue Xiangyang

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9577055/9577056/09577779.pdf?arnumber=9577779

Cited by 34 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. Advancing Few-Shot Black-Box Attack With Alternating Training;IEEE Transactions on Reliability;2024-09

2. Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images;International Journal of Computer Vision;2024-07-08

3. Multi-attribute Semantic Adversarial Attack Based on Cross-layer Interpolation for Face Recognition;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. STMS: An Out-Of-Distribution Model Stealing Method Based on Causality;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Poisoning-Free Defense Against Black-Box Model Extraction;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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