1 code implementation • 11 May 2023 • Yinghua Gao, Yiming Li, Xueluan Gong, Zhifeng Li, Shu-Tao Xia, Qian Wang
More importantly, it is not feasible to simply combine existing methods to design an effective sparse and invisible backdoor attack.
no code implementations • 22 Feb 2022 • Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
To explore whether adversarial training could defend against backdoor attacks or not, we conduct extensive experiments across different threat models and perturbation budgets, and find the threat model in adversarial training matters.
no code implementations • 29 Sep 2021 • Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
Based on thorough experiments, we find that such trade-off ignores the interactions between the perturbation budget of adversarial training and the magnitude of the backdoor trigger.
1 code implementation • NeurIPS 2020 • Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.