50 citations · 259 across the 28 of their papers we have counts for
20 papers · 1 filter
Membership Inference Attack Against Masked Image Modeling
Zheng Li, Xinlei He, Ning Yu +1
Masked Image Modeling (MIM) has achieved significant success in the realm of self-supervised learning (SSL) for visual recognition. The image encoder pre-trained through MIM, invol…
Link Stealing Attacks Against Inductive Graph Neural Networks
Yixin Wu, Xinlei He, Pascal Berrang +4
A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, includi…
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
Boyang Zhang, Zheng Li, Ziqing Yang +4
While advanced machine learning (ML) models are deployed in numerous real-world applications, previous works demonstrate these models have security and privacy vulnerabilities. Var…
A Comprehensive Study of Privacy Risks in Curriculum Learning
Joann Qiongna Chen, Xinlei He, Zheng Li +2
Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achievin…
Test-Time Poisoning Attacks Against Test-Time Adaptation Models
Tianshuo Cong, Xinlei He, Yun Shen +1
Deploying machine learning (ML) models in the wild is challenging as it suffers from distribution shifts, where the model trained on an original domain cannot generalize well to un…
Generated Graph Detection
Yihan Ma, Zhikun Zhang, Ning Yu +4
Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misus…
MGTBench: Benchmarking Machine-Generated Text Detection
Xinlei He, Xinyue Shen, Zeyuan Chen +2
Nowadays, powerful large language models (LLMs) such as ChatGPT have demonstrated revolutionary power in a variety of tasks. Consequently, the detection of machine-generated texts…
A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots
Boyang Zhang, Xinlei He, Yun Shen +2
Building advanced machine learning (ML) models requires expert knowledge and many trials to discover the best architecture and hyperparameter settings. Previous work demonstrates t…
Fine-Tuning Is All You Need to Mitigate Backdoor Attacks
Zeyang Sha, Xinlei He, Pascal Berrang +2
Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increas…
Backdoor Attacks in the Supply Chain of Masked Image Modeling
Xinyue Shen, Xinlei He, Zheng Li +3
Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for image pre-training. In contrast to previous dominating self-supervised methods, i.e., contrastive lear…
Data Poisoning Attacks Against Multimodal Encoders
Ziqing Yang, Xinlei He, Zheng Li +4
Recently, the newly emerged multimodal models, which leverage both visual and linguistic modalities to train powerful encoders, have gained increasing attention. However, learning…
Auditing Membership Leakages of Multi-Exit Networks
Zheng Li, Yiyong Liu, Xinlei He +3
Relying on the fact that not all inputs require the same amount of computation to yield a confident prediction, multi-exit networks are gaining attention as a prominent approach fo…
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu +2
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to infer whether an input sample w…
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
Xinlei He, Hongbin Liu, Neil Zhenqiang Gong +1
Semi-supervised learning (SSL) leverages both labeled and unlabeled data to train machine learning (ML) models. State-of-the-art SSL methods can achieve comparable performance to s…
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders
Tianshuo Cong, Xinlei He, Yang Zhang
Self-supervised learning is an emerging machine learning paradigm. Compared to supervised learning which leverages high-quality labeled datasets, self-supervised learning relies on…
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
Zeyang Sha, Xinlei He, Ning Yu +2
Self-supervised representation learning techniques have been developing rapidly to make full use of unlabeled images. They encode images into rich features that are oblivious to do…
Model Stealing Attacks Against Inductive Graph Neural Networks
Yun Shen, Xinlei He, Yufei Han +1
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to buil…
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…
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He +6
Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied,…
Stealing Links from Graph Neural Networks
Xinlei He, Jinyuan Jia, Michael Backes +2
Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data…