False Data Injection Attack on Power System Data- Driven Methods Based on Generative Adversarial Networks
Author:
Affiliation:
1. Southeast University,The College of Software Engineering,Nanjing,China
Funder
State Grid Corporation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9735429/9735430/09735442.pdf?arnumber=9735442
Reference8 articles.
1. Explaining and Harnessing Adversarial Examples[J];goodfellow,2015
2. Intriguing properties of neural networks[C];szegedy;2nd International Conference on Learning Representations ICLR 2014,2014
3. Robust Physical-World Attacks on Deep Learning Visual Classification
4. One Pixel Attack for Fooling Deep Neural Networks
5. Generative adversarial nets[J];goodfellow;Advances in neural information processing systems,2014
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