De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks

8 May 2021

Papers citing "De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks"

7 / 7 papers shown

Title
Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs Himanshu Beniwal Sailesh Panda Birudugadda Srivibhav Mayank Singh 45 0 0 24 Feb 2025
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models Weijiao Zhang Jindong Han Zhao Xu Hang Ni Hao Liu Hui Xiong Hui Xiong AI4CE 77 15 0 30 Jan 2024
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning Hongbin Liu Wenjie Qu Jinyuan Jia Neil Zhenqiang Gong SSL 28 6 0 06 Dec 2022
Federated Learning based on Defending Against Data Poisoning Attacks in IoT Jiayin Li Wenzhong Guo Xingshuo Han Jianping Cai Ximeng Liu AAML 83 1 0 14 Sep 2022
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning Hongbin Liu Jinyuan Jia Neil Zhenqiang Gong 25 34 0 13 May 2022
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey M. A. Ramírez Song-Kyoo Kim H. A. Hamadi Ernesto Damiani Young-Ji Byon Tae-Yeon Kim C. Cho C. Yeun AAML 22 37 0 21 Feb 2022
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers Xi Li David J. Miller Zhen Xiang G. Kesidis AAML 16 0 0 28 May 2021