Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff-Reference-Cited by-同舟云学术

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Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff

Published:2021-06-06 Issue: Volume: Page:
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Container-title:ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Borgnia Eitan,Cherepanova Valeriia,Fowl Liam,Ghiasi Amin,Geiping Jonas,Goldblum Micah,Goldstein Tom,Gupta Arjun

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9413349/9413350/09414862.pdf?arnumber=9414862

Cited by 42 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. Data Poisoning and Leakage Analysis in Federated Learning;Springer Optimization and Its Applications;2024-09-04

2. SoK: Unintended Interactions among Machine Learning Defenses and Risks;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Survey: Image mixing and deleting for data augmentation;Engineering Applications of Artificial Intelligence;2024-05

4. DACO-BD: Data Augmentation Combinatorial Optimization-Based Backdoor Defense in Deep Neural Networks for SAR Image Classification;IEEE Transactions on Instrumentation and Measurement;2024

5. NeuralSanitizer: Detecting Backdoors in Neural Networks;IEEE Transactions on Information Forensics and Security;2024

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