Lin, Weiran, Lucas, Keane, Bauer, Lujo, Reiter, Michael K., and Sharif, Mahmood. Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. Retrieved from https://par.nsf.gov/biblio/10353969. Proceedings of Machine Learning Research 162.
Lin, Weiran, Lucas, Keane, Bauer, Lujo, Reiter, Michael K., & Sharif, Mahmood. Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. Proceedings of Machine Learning Research, 162 (). Retrieved from https://par.nsf.gov/biblio/10353969.
Lin, Weiran, Lucas, Keane, Bauer, Lujo, Reiter, Michael K., and Sharif, Mahmood.
"Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks". Proceedings of Machine Learning Research 162 (). Country unknown/Code not available. https://par.nsf.gov/biblio/10353969.
@article{osti_10353969,
place = {Country unknown/Code not available},
title = {Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks},
url = {https://par.nsf.gov/biblio/10353969},
abstractNote = {},
journal = {Proceedings of Machine Learning Research},
volume = {162},
author = {Lin, Weiran and Lucas, Keane and Bauer, Lujo and Reiter, Michael K. and Sharif, Mahmood},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.