14 citations · 15 across the 6 of their papers we have counts for
7 papers
Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy
Puning Zhao, Zhikun Zhang, Shaowei Wang +5
Local differential privacy (LDP) protocols are vulnerable to poisoning attacks. Existing research have proposed efficient defense strategies for single-item users. However, in prac…
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial attacks. However, FAT is prone…
Mitigating Error Amplification in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Bo Wang +3
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations. However, FAT often suffe…
FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
Tianhang Zheng, Yanlu Li, Bohan Deng +1
Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effectiveness, it is vulnerable to poisonin…
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Tianhang Zheng, Di Wang, Baochun Li +1
As a certified defensive technique, randomized smoothing has received considerable attention due to its scalability to large datasets and neural networks. However, several importan…
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition
Tianhang Zheng, Sheng Liu, Changyou Chen +3
Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances and potential for broad applications such as autonomous…