7 citations · 21 across the 13 of their papers we have counts for
5 papers · 1 filter
Data-Free Universal Attack by Exploiting the Intrinsic Vulnerability of Deep Models
YangTian Yan, Jinyu Tian
Deep neural networks (DNNs) are susceptible to Universal Adversarial Perturbations (UAPs), which are instance agnostic perturbations that can deceive a target model across a wide r…
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization
Yuhao He, Jinyu Tian, Xianwei Zheng +3
Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional po…
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain
Fengpeng Li, Kemou Li, Haiwei Wu +2
To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent stud…
Regroup Median Loss for Combating Label Noise
Fengpeng Li, Kemou Li, Jinyu Tian +1
The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect an…
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
Jinyu Tian, Jiantao Zhou, Yuanman Li +1
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors. In this work,…