With False Friends Like These, Who Can Notice Mistakes?

29 December 2020

Papers citing "With False Friends Like These, Who Can Notice Mistakes?"

9 / 9 papers shown

Title
Squeeze Training for Adversarial Robustness Qizhang Li Yiwen Guo W. Zuo Hao Chen OOD 39 9 0 23 May 2022
Can Adversarial Training Be Manipulated By Non-Robust Features? Lue Tao Lei Feng Hongxin Wei Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 83 16 0 31 Jan 2022
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 46 100 0 07 Oct 2021
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 156 190 0 13 Jan 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 256 488 0 31 Dec 2020
Unadversarial Examples: Designing Objects for Robust Vision Hadi Salman Andrew Ilyas Logan Engstrom Sai H. Vemprala A. Madry Ashish Kapoor WIGM 64 59 0 22 Dec 2020
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks Akhilan Boopathy Tsui-Wei Weng Pin-Yu Chen Sijia Liu Luca Daniel AAML 108 138 0 29 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 240 1,837 0 03 Feb 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016