Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review
Authors: Huda Ali Alatwi, Charles Morisset | Published: 2021-12-06
Poisoning
When the Curious Abandon Honesty: Federated Learning Is Not Private
Authors: Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot | Published: 2021-12-06 | Updated: 2023-04-12
Improving Differentially Private SGD via Randomly Sparsified Gradients
Authors: Junyi Zhu, Matthew B. Blaschko | Published: 2021-12-01 | Updated: 2023-06-28
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
Authors: Utku Ozbulak, Maura Pintor, Arnout Van Messem, Wesley De Neve | Published: 2021-11-22
Fooling Adversarial Training with Inducing Noise
Authors: Zhirui Wang, Yifei Wang, Yisen Wang | Published: 2021-11-19
Enhanced Membership Inference Attacks against Machine Learning Models
Authors: Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, Reza Shokri | Published: 2021-11-18 | Updated: 2022-09-13
Bayesian Framework for Gradient Leakage
Authors: Mislav Balunović, Dimitar I. Dimitrov, Robin Staab, Martin Vechev | Published: 2021-11-08 | Updated: 2022-03-17
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Authors: Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang | Published: 2021-11-08
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Authors: Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong | Published: 2021-11-04
Efficient passive membership inference attack in federated learning
Authors: Oualid Zari, Chuan Xu, Giovanni Neglia | Published: 2021-10-31