5 citations · 9 across the 7 of their papers we have counts for
7 papers
Dummy-Aware Weighted Attack (DAWA): Breaking the Safe Sink in Dummy Class Defenses
Yunrui Yu, Xuxiang Feng, Pengda Qin +5
Adversarial robustness evaluation faces a critical challenge as new defense paradigms emerge that can exploit limitations in existing assessment methods. This paper reveals that Du…
Why the Maximum Second Derivative of Activations Matters for Adversarial Robustness
Yunrui Yu, Hang Su, Jun Zhu
This work investigates the critical role of activation function curvature -- quantified by the maximum second derivative -- in adversarial robustness. Using the Recursi…
RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function
Yunrui Yu, Kafeng Wang, Hang Su +1
Despite their widespread success, deep neural networks remain critically vulnerable to adversarial attacks, posing significant risks in safety-sensitive applications. This paper in…
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Yunrui Yu, Hang Su, Cheng-zhong Xu +2
Gradient-based adversarial attacks using the Cross-Entropy (CE) loss often suffer from overestimation due to relative errors in gradient computation induced by floating-point arith…
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack
Yunrui Yu, Xitong Gao, Cheng-Zhong Xu
Adversarial attacks can deceive neural networks by adding tiny perturbations to their input data. Ensemble defenses, which are trained to minimize attack transferability among sub-…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
Yunrui Yu, Xitong Gao, Cheng-Zhong Xu
Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This…