Minimax Defense against Gradient-based Adversarial Attacks
Authors: Blerta Lindqvist, Rauf Izmailov | Published: 2020-02-04
Adversarial Machine Learning — Industry Perspectives
Authors: Ram Shankar Siva Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, Sharon Xia | Published: 2020-02-04 | Updated: 2021-03-19
Defending Adversarial Attacks via Semantic Feature Manipulation
Authors: Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen | Published: 2020-02-03 | Updated: 2020-04-22
Learning to Detect Malicious Clients for Robust Federated Learning
Authors: Suyi Li, Yong Cheng, Wei Wang, Yang Liu, Tianjian Chen | Published: 2020-02-01
Politics of Adversarial Machine Learning
Authors: Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar | Published: 2020-02-01 | Updated: 2020-04-26
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners
Authors: Amir Andalib, Vahid Tabataba Vakili | Published: 2020-01-31 | Updated: 2020-12-29
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications
Authors: Dou Goodman, Lv Zhonghou, Wang minghua | Published: 2020-01-31
Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
Authors: Yigit Alparslan, Ken Alparslan, Jeremy Keim-Shenk, Shweta Khade, Rachel Greenstadt | Published: 2020-01-30 | Updated: 2021-02-08
A4 : Evading Learning-based Adblockers
Authors: Shitong Zhu, Zhongjie Wang, Xun Chen, Shasha Li, Umar Iqbal, Zhiyun Qian, Kevin S. Chan, Srikanth V. Krishnamurthy, Zubair Shafiq | Published: 2020-01-29
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance
Authors: Farhad Farokhi | Published: 2020-01-29