Defenses in Adversarial Machine Learning: A Survey
Authors: Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng, Zihao Zhu, Mingda Zhang, Hongrui Chen, Danni Yuan, Li Liu, Qingshan Liu | Published: 2023-12-13
防御手法
Defense against ML-based Power Side-channel Attacks on DNN Accelerators with Adversarial Attacks
Authors: Xiaobei Yan, Chip Hong Chang, Tianwei Zhang | Published: 2023-12-07
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Authors: Chi Zhang, Zifan Wang, Ravi Mangal, Matt Fredrikson, Limin Jia, Corina Pasareanu | Published: 2023-11-22
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models
Authors: Yuanbin Cheng, Koji Yamashita, Jim Follum, Nanpeng Yu | Published: 2023-11-13
Seeing is Believing: A Federated Learning Based Prototype to Detect Wireless Injection Attacks
Authors: Aadil Hussain, Nitheesh Gundapu, Sarang Drugkar, Suraj Kiran, J. Harshan, Ranjitha Prasad | Published: 2023-11-11
Does Differential Privacy Prevent Backdoor Attacks in Practice?
Authors: Fereshteh Razmi, Jian Lou, Li Xiong | Published: 2023-11-10
Input Reconstruction Attack against Vertical Federated Large Language Models
Authors: Fei Zheng | Published: 2023-11-07 | Updated: 2023-11-24
PubDef: Defending Against Transfer Attacks From Public Models
Authors: Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David Wagner | Published: 2023-10-26 | Updated: 2024-03-17
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Authors: Caelin G. Kaplan, Chuan Xu, Othmane Marfoq, Giovanni Neglia, Anderson Santana de Oliveira | Published: 2023-10-18
Assessing Robustness via Score-Based Adversarial Image Generation
Authors: Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann | Published: 2023-10-06