76 citations · 292 across the 19 of their papers we have counts for
19 papers
Adversarial Example Based Fingerprinting for Robust Copyright Protection in Split Learning
Zhangting Lin, Mingfu Xue, Kewei Chen +5
Currently, deep learning models are easily exposed to data leakage risks. As a distributed model, Split Learning thus emerged as a solution to address this issue. The model is spli…
Turn Passive to Active: A Survey on Active Intellectual Property Protection of Deep Learning Models
Mingfu Xue, Leo Yu Zhang, Yushu Zhang +1
The intellectual property protection of deep learning (DL) models has attracted increasing serious concerns. Many works on intellectual property protection for Deep Neural Networks…
InFIP: An Explainable DNN Intellectual Property Protection Method based on Intrinsic Features
Mingfu Xue, Xin Wang, Yinghao Wu +3
Intellectual property (IP) protection for Deep Neural Networks (DNNs) has raised serious concerns in recent years. Most existing works embed watermarks in the DNN model for IP prot…
Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion
Yushu Zhang, Jiahao Zhu, Mignfu Xue +2
Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven…
Detecting Recolored Image by Spatial Correlation
Yushu Zhang, Nuo Chen, Shuren Qi +2
Image forensics, aiming to ensure the authenticity of the image, has made great progress in dealing with common image manipulation such as copy-move, splicing, and inpainting in th…
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks
Mingfu Xue, Shifeng Ni, Yinghao Wu +3
Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks. The backdoored DNN model will behave maliciously when images containing bac…
Compression-Resistant Backdoor Attack against Deep Neural Networks
Mingfu Xue, Xin Wang, Shichang Sun +3
In recent years, many backdoor attacks based on training data poisoning have been proposed. However, in practice, those backdoor attacks are vulnerable to image compressions. When…
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…
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations
Mingfu Xue, Yinghao Wu, Zhiyu Wu +3
Recent researches show that deep learning model is susceptible to backdoor attacks. Many defenses against backdoor attacks have been proposed. However, existing defense works requi…
Detect and remove watermark in deep neural networks via generative adversarial networks
Haoqi Wang, Mingfu Xue, Shichang Sun +3
Deep neural networks (DNN) have achieved remarkable performance in various fields. However, training a DNN model from scratch requires a lot of computing resources and training dat…
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…
Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images
Shichang Sun, Mingfu Xue, Jian Wang +1
Recently, the research on protecting the intellectual properties (IP) of deep neural networks (DNN) has attracted serious concerns. A number of DNN copyright protection methods hav…
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…
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…