Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review
Authors: Mathias Lundteigen Mohus, Jinyue Li | Published: 2023-06-01
ポイズニング
Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective
Authors: Andi Zhang, Mingtian Zhang, Damon Wischik | Published: 2023-06-01 | Updated: 2024-11-24
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Authors: Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu | Published: 2023-04-11
Balancing Privacy and Performance for Private Federated Learning Algorithms
Authors: Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horvath | Published: 2023-04-11 | Updated: 2023-08-18
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
Authors: Edoardo Gabrielli, Dimitri Belli, Zoe Matrullo, Vittorio Miori, Gabriele Tolomei | Published: 2023-03-29 | Updated: 2024-12-02
A Survey on Malware Detection with Graph Representation Learning
Authors: Tristan Bilot, Nour El Madhoun, Khaldoun Al Agha, Anis Zouaoui | Published: 2023-03-28 | Updated: 2023-08-17
The Devil’s Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models
Authors: Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie | Published: 2023-03-15 | Updated: 2024-01-11
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
Authors: Yiwei Lu, Gautam Kamath, Yaoliang Yu | Published: 2023-03-07 | Updated: 2023-06-06
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Authors: Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal | Published: 2023-02-21 | Updated: 2023-07-20
Poisoning Web-Scale Training Datasets is Practical
Authors: Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr | Published: 2023-02-20 | Updated: 2024-05-06