114 citations · 519 across the 20 of their papers we have counts for
20 papers
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…
Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
Tao Wang, Yushu Zhang, Zixuan Yang +3
Massive captured face images are stored in the database for the identification of individuals. However, these images can be observed unintentionally by data managers, which is not…
Spatial-Frequency Discriminability for Revealing Adversarial Perturbations
Chao Wang, Shuren Qi, Zhiqiu Huang +4
The vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. From a security perspective, it poses a critical…
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…
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
Chao Wang, Zhiqiu Huang, Shuren Qi +3
Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic method…
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…
Metaverse: Security and Privacy Concerns
Ruoyu Zhao, Yushu Zhang, Youwen Zhu +2
The term "metaverse", a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is pos…
A Principled Design of Image Representation: Towards Forensic Tasks
Shuren Qi, Yushu Zhang, Chao Wang +2
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on…
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…
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…
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation
Shuren Qi, Yushu Zhang, Chao Wang +2
Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents.…
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…