34 citations · 82 across the 7 of their papers we have counts for
7 papers
FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations
Can He, Lingxiao Meng, Zhirui Sun +2
Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric folding manipulation…
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
Mingfu Xue, Can He, Shichang Sun +2
Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create…
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples
Mingfu Xue, Shichang Sun, Can He +3
The training of Deep Neural Networks (DNN) is costly, thus DNN can be considered as the intellectual properties (IP) of model owners. To date, most of the existing protection works…
3D Invisible Cloak
Mingfu Xue, Can He, Zhiyu Wu +3
In this paper, we propose a novel physical stealth attack against the person detectors in real world. The proposed method generates an adversarial patch, and prints it on real clot…
Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks
Mingfu Xue, Chengxiang Yuan, Can He +4
Recently, the membership inference attack poses a serious threat to the privacy of confidential training data of machine learning models. This paper proposes a novel adversarial ex…
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images
Mingfu Xue, Shichang Sun, Zhiyu Wu +3
The popularity of various social platforms has prompted more people to share their routine photos online. However, undesirable privacy leakages occur due to such online photo shari…
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors
Mingfu Xue, Chengxiang Yuan, Can He +2
In this paper, we propose a natural and robust physical adversarial example attack method targeting object detectors under real-world conditions. The generated adversarial examples…