50 citations · 161 across the 9 of their papers we have counts for
22 papers
Amplifying Membership Exposure via Data Poisoning
Yufei Chen, Chao Shen, Yun Shen +2
As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to…
Membership Inference Attacks Against Text-to-image Generation Models
Yixin Wu, Ning Yu, Zheng Li +2
Text-to-image generation models have recently attracted unprecedented attention as they unlatch imaginative applications in all areas of life. However, developing such models requi…
Get a Model! Model Hijacking Attack Against Machine Learning Models
Ahmed Salem, Michael Backes, Yang Zhang
Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems. However, with this increa…
Membership Inference Attacks Against Recommender Systems
Minxing Zhang, Zhaochun Ren, Zihan Wang +4
Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly…
Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu +3
Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine l…
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He, Yang Zhang
Data is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive…