Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things
Authors: Maede Zolanvari, Marcio A. Teixeira, Lav Gupta, Khaled M. Khan, Raj Jain | Published: 2019-11-13
バックドア攻撃
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Authors: Lixu Wang, Shichao Xu, Xiao Wang, Qi Zhu | Published: 2019-10-14 | Updated: 2019-10-27
Defending Neural Backdoors via Generative Distribution Modeling
Authors: Ximing Qiao, Yukun Yang, Hai Li | Published: 2019-10-10 | Updated: 2019-11-06
Detecting AI Trojans Using Meta Neural Analysis
Authors: Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, Bo Li | Published: 2019-10-08 | Updated: 2020-10-01
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Authors: Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian | Published: 2019-10-01
Hidden Trigger Backdoor Attacks
Authors: Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash | Published: 2019-09-30 | Updated: 2019-12-21
Deep k-NN Defense against Clean-label Data Poisoning Attacks
Authors: Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson | Published: 2019-09-29 | Updated: 2020-08-13
Detection of Backdoors in Trained Classifiers Without Access to the Training Set
Authors: Zhen Xiang, David J. Miller, George Kesidis | Published: 2019-08-27 | Updated: 2020-08-19
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
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