  With the rapid advancement of diffusion-based image-generative models, the
quality of generated images has become increasingly photorealistic. Moreover,
with the release of high-quality pre-trained image-generative models, a growing
number of users are downloading these pre-trained models to fine-tune them with
downstream datasets for various image-generation tasks. However, employing such
powerful pre-trained models in downstream tasks presents significant privacy
leakage risks. In this paper, we propose the first reconstruction-based
membership inference attack framework, tailored for recent diffusion models,
and in the more stringent black-box access setting. Considering four distinct
attack scenarios and three types of attacks, this framework is capable of
targeting any popular conditional generator model, achieving high precision,
evidenced by an impressive AUC of $0.95$.
