294 citations · 433 across the 18 of their papers we have counts for
5 papers · 1 filter
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu +2
Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (F…
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to…
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu +2
Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised lea…
Non-Singular Adversarial Robustness of Neural Networks
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving t…
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu
Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations. Very recently, Ma…