300 citations · 1.6k across the 40 of their papers we have counts for
44 papers
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…
White-box Membership Inference Attacks against Diffusion Models
Yan Pang, Tianhao Wang, Xuhui Kang +2
Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance. The complex architecture o…
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
Xinyue Shen, Zeyuan Chen, Michael Backes +2
The misuse of large language models (LLMs) has drawn significant attention from the general public and LLM vendors. One particular type of adversarial prompt, known as jailbreak pr…
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark
Jun Niu, Xiaoyan Zhu, Moxuan Zeng +15
Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. However, it has been increasingly recogni…
Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
Yiting Qu, Xinyue Shen, Xinlei He +3
State-of-the-art Text-to-Image models like Stable Diffusion and DALLE2 are revolutionizing how people generate visual content. At the same time, society has serious concerns…
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT
Xinyue Shen, Zeyuan Chen, Michael Backes +1
The way users acquire information is undergoing a paradigm shift with the advent of ChatGPT. Unlike conventional search engines, ChatGPT retrieves knowledge from the model itself a…
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…
Prompt Stealing Attacks Against Text-to-Image Generation Models
Xinyue Shen, Yiting Qu, Michael Backes +1
Text-to-Image generation models have revolutionized the artwork design process and enabled anyone to create high-quality images by entering text descriptions called prompts. Creati…
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…
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…
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
Zeyang Sha, Zheng Li, Ning Yu +1
Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encoura…
Membership Inference Attacks by Exploiting Loss Trajectory
Yiyong Liu, Zhengyu Zhao, Michael Backes +1
Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model's…
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…
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…
Property Inference Attacks Against GANs
Junhao Zhou, Yufei Chen, Chao Shen +1
While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks. So…
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…
Graph Unlearning
Min Chen, Zhikun Zhang, Tianhao Wang +3
Machine unlearning is a process of removing the impact of some training data from the machine learning (ML) models upon receiving removal requests. While straightforward and legiti…
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…
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,…
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models
Ahmed Salem, Yannick Sautter, Michael Backes +2
The tremendous progress of autoencoders and generative adversarial networks (GANs) has led to their application to multiple critical tasks, such as fraud detection and sanitized da…