Model Agnostic Defence against Backdoor Attacks in Machine Learning
Authors: Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, Sudipta Chattopadhyay | Published: 2019-08-06 | Updated: 2022-03-31
バックドア攻撃
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Authors: Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski | Published: 2019-07-17 | Updated: 2019-10-03
Poisoning Attacks with Generative Adversarial Nets
Authors: Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo, Javier Carnerero-Cano, Emil C. Lupu | Published: 2019-06-18 | Updated: 2019-09-25
On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks
Authors: Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, Xinda Li, Florian Kerschbaum | Published: 2019-06-18 | Updated: 2019-11-26
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Authors: Adnan Qayyum, Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha | Published: 2019-05-29
An Investigation of Data Poisoning Defenses for Online Learning
Authors: Yizhen Wang, Somesh Jha, Kamalika Chaudhuri | Published: 2019-05-28 | Updated: 2020-02-19
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Authors: Liwei Song, Reza Shokri, Prateek Mittal | Published: 2019-05-24 | Updated: 2019-08-25
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Authors: Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein | Published: 2019-05-15 | Updated: 2019-05-16
A Robust Approach for Securing Audio Classification Against Adversarial Attacks
Authors: Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich | Published: 2019-04-24 | Updated: 2019-11-25
Gotta Catch ‘Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Authors: Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, Ben Y. Zhao | Published: 2019-04-18 | Updated: 2020-09-28