190 citations · 260 across the 19 of their papers we have counts for
5 papers · 1 filter
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Xuanqi Gao, Juan Zhai, Shiqing Ma +3
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Cl…
UNICORN: A Unified Backdoor Trigger Inversion Framework
Zhenting Wang, Kai Mei, Juan Zhai +1
The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (D…
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
Xuanqi Gao, Juan Zhai, Shiqing Ma +3
With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical perfo…
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
Zhenting Wang, Hailun Ding, Juan Zhai +1
The backdoor or Trojan attack is a severe threat to deep neural networks (DNNs). Researchers find that DNNs trained on benign data and settings can also learn backdoor behaviors, w…
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry
Yingqi Liu, Guangyu Shen, Guanhong Tao +3
Backdoor attack injects malicious behavior to models such that inputs embedded with triggers are misclassified to a target label desired by the attacker. However, natural features…