58 citations · 305 across the 33 of their papers we have counts for
6 papers · 1 filter
Defending against Model Stealing via Verifying Embedded External Features
Yiming Li, Linghui Zhu, Xiaojun Jia +3
Obtaining a well-trained model involves expensive data collection and training procedures, therefore the model is a valuable intellectual property. Recent studies revealed that adv…
Clustering Effect of (Linearized) Adversarial Robust Models
Yang Bai, Xin Yan, Yong Jiang +2
Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness aga…
Backdoor Attack in the Physical World
Yiming Li, Tongqing Zhai, Yong Jiang +2
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of infected models will be maliciously changed if the hidden backdo…
Hidden Backdoor Attack against Semantic Segmentation Models
Yiming Li, Yanjie Li, Yalei Lv +2
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data. The attacked model behaves nor…
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
Chaobing Song, Zhengyuan Zhou, Yichao Zhou +2
The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs…
Improving Adversarial Robustness via Channel-wise Activation Suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang +3
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing…