4 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.CV2025
PRBench: A Standardized Probabilistic Robustness Benchmark
Yi Zhang, Zheng Wang, Zhen Chen +5
Deep learning models are notoriously vulnerable to imperceptible perturbations. Most existing research centers on adversarial robustness (AR), which evaluates models under worst-ca…
cs.LG2025★ 1 cited
Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
Lucas Fenaux, Zheng Wang, Jacob Yan +2
Federated Learning (FL) enables distributed model training but is vulnerable to backdoor attacks, where malicious clients embed attacker-controlled behaviors into the global model.…
cs.CV2022★ 4 cited
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Zheng Wang, Wenjie Ruan
Recent research on the robustness of deep learning has shown that Vision Transformers (ViTs) surpass the Convolutional Neural Networks (CNNs) under some perturbations, e.g., natura…