114 citations · 519 across the 20 of their papers we have counts for
6 papers · 1 filter
Security and Privacy on Generative Data in AIGC: A Survey
Tao Wang, Yushu Zhang, Shuren Qi +3
The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate h…
Protect the Intellectual Property of Dataset against Unauthorized Use
Mingfu Xue, Yinghao Wu, Yushu Zhang +2
Training high performance Deep Neural Networks (DNNs) models require large-scale and high-quality datasets. The expensive cost of collecting and annotating large-scale datasets mak…
AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption
Mingfu Xue, Zhiyu Wu, Jian Wang +2
A well-trained DNN model can be regarded as an intellectual property (IP) of the model owner. To date, many DNN IP protection methods have been proposed, but most of them are water…
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…
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…
Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations
Mingfu Xue, Yushu Zhang, Jian Wang +1
The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obta…