most citedPrivacy Threats in Stable Diffusion Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2025

ConQuER: Modular Architectures for Control and Bias Mitigation in IQP Quantum Generative Models

Xiaocheng Zou, Shijin Duan, Charles Fleming +4

Quantum generative models based on instantaneous quantum polynomial (IQP) circuits show great promise in learning complex distributions while maintaining classical trainability. Ho…

cs.CR2025

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks

Kaiyuan Zhang, Siyuan Cheng, Hanxi Guo +8

Large language models (LLMs) have achieved remarkable success and are widely adopted for diverse applications. However, fine-tuning these models often involves private or sensitive…

cs.CV2025

Targeted Forgetting of Image Subgroups in CLIP Models

Zeliang Zhang, Gaowen Liu, Charles Fleming +2

Foundation models (FMs) such as CLIP have demonstrated impressive zero-shot performance across various tasks by leveraging large-scale, unsupervised pre-training. However, they oft…

cs.SD2025

Enhancing Dance-to-Music Generation via Negative Conditioning Latent Diffusion Model

Changchang Sun, Gaowen Liu, Charles Fleming +1

Conditional diffusion models have gained increasing attention since their impressive results for cross-modal synthesis, where the strong alignment between conditioning input and ge…

cs.CV20231 cited

Privacy Threats in Stable Diffusion Models

Thomas Cilloni, Charles Fleming, Charles Walter

This paper introduces a novel approach to membership inference attacks (MIA) targeting stable diffusion computer vision models, specifically focusing on the highly sophisticated St…