  This paper introduces a novel approach to membership inference attacks (MIA)
targeting stable diffusion computer vision models, specifically focusing on the
highly sophisticated Stable Diffusion V2 by StabilityAI. MIAs aim to extract
sensitive information about a model's training data, posing significant privacy
concerns. Despite its advancements in image synthesis, our research reveals
privacy vulnerabilities in the stable diffusion models' outputs. Exploiting
this information, we devise a black-box MIA that only needs to query the victim
model repeatedly. Our methodology involves observing the output of a stable
diffusion model at different generative epochs and training a classification
model to distinguish when a series of intermediates originated from a training
sample or not. We propose numerous ways to measure the membership features and
discuss what works best. The attack's efficacy is assessed using the ROC AUC
method, demonstrating a 60\% success rate in inferring membership information.
This paper contributes to the growing body of research on privacy and security
in machine learning, highlighting the need for robust defenses against MIAs.
Our findings prompt a reevaluation of the privacy implications of stable
diffusion models, urging practitioners and developers to implement enhanced
security measures to safeguard against such attacks.
