5 citations · 8 across the 5 of their papers we have counts for
5 papers
TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
Quang Duc Nguyen, Siyuan Liang, Yiming Li +2
Time Series Forecasting (TSF) is highly vulnerable to backdoor attacks, yet effective defenses remain underexplored due to challenges arising from data entanglement and shifts in t…
Cert-SSBD: Certified Backdoor Defense with Sample-Specific Smoothing Noises
Ting Qiao, Yingjia Wang, Xing Liu +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where an attacker manipulates a small portion of the training data to implant hidden backdoors into the model. The c…
FLARE: Toward Universal Dataset Purification against Backdoor Attacks
Linshan Hou, Wei Luo, Zhongyun Hua +3
Deep neural networks (DNNs) are susceptible to backdoor attacks, where adversaries poison datasets with adversary-specified triggers to implant hidden backdoors, enabling malicious…
IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
Linshan Hou, Ruili Feng, Zhongyun Hua +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model trai…
Circumventing Backdoor Defenses That Are Based on Latent Separability
Xiangyu Qi, Tinghao Xie, Yiming Li +2
Recent studies revealed that deep learning is susceptible to backdoor poisoning attacks. An adversary can embed a hidden backdoor into a model to manipulate its predictions by only…