Membership Inference Attacks Against Text-to-image Generation Models
Text-to-image generation models have recently attracted unprecedented attention as they unlatch imaginative applications in all areas of life. However, developing such models requires huge amounts of data that might contain privacy-sensitive information, e.g., face identity. While privacy risks have been extensively demonstrated in the image classification and GAN generation domains, privacy risks in the text-to-image generation domain are largely unexplored. In this paper, we perform the first privacy analysis of text-to-image generation models through the lens of membership inference. Specifically, we propose three key intuitions about membership information and design four attack methodologies accordingly. We conduct comprehensive evaluations on two mainstream text-to-image generation models including sequence-to-sequence modeling and diffusion-based modeling. The empirical results show that all of the proposed attacks can achieve significant performance, in some cases even close to an accuracy of 1, and thus the corresponding risk is much more severe than that shown by existing membership inference attacks. We further conduct an extensive ablation study to analyze the factors that may affect the attack performance, which can guide developers and researchers to be alert to vulnerabilities in text-to-image generation models. All these findings indicate that our proposed attacks pose a realistic privacy threat to the text-to-image generation models.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationImage GenerationText to Image GenerationText-to-Image GenerationSimilar Papers 제목 키워드 기반
Reconstruction and Membership Inference Attacks against Generative Models
We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative m…
Density EstimationInference AttackMembership Inference AttackImage Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models
Large vision-language models (LVLMs) have demonstrated outstanding performance in many downstream tasks. However, LVLMs are trained on large-scale datasets, which can pose privacy risks if training images contain sensiti…
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
While being deployed in many critical applications as core components, machine learning (ML) models are vulnerable to various security and privacy attacks. One major privacy attack in this domain is membership inference,…
BIG-bench Machine LearningTransfer LearningMembership Leakage in Label-Only Exposures
Machine learning (ML) has been widely adopted in various privacy-critical applications, e.g., face recognition and medical image analysis. However, recent research has shown that ML models are vulnerable to attacks again…
Face RecognitionInference AttackMedical Image AnalysisNode-Level Membership Inference Attacks Against Graph Neural Networks
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 learning (ML) models, namely graph neural…
BIG-bench Machine Learning