Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy

TL;DR

This paper systematically analyzes privacy risks in diffusion models, introduces new membership inference attacks tailored to these models, and demonstrates their high effectiveness in real-world scenarios.

Contribution

It provides the first comprehensive analysis of membership inference attacks specific to diffusion models and proposes novel, highly effective attack methods.

Findings

Achieves near-perfect attack performance (>0.9 AUCROC)

Demonstrates significant privacy risks in diffusion models

Highlights need for privacy safeguards in image generation

Abstract

In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications. Despite the numerous benefits brought by recent advances in diffusion models, there are also concerns about their potential misuse, specifically in terms of privacy breaches and intellectual property infringement. In particular, some of their unique characteristics open up new attack surfaces when considering the real-world deployment of such models. With a thorough investigation of the attack vectors, we develop a systematic analysis of membership inference attacks on diffusion models and propose novel attack methods tailored to each attack scenario specifically relevant to diffusion models. Our approach exploits easily obtainable quantities and is highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in realistic scenarios. Our extensive experiments demonstrate the effectiveness of our method, highlighting the importance of considering privacy and intellectual property risks when using diffusion models in image generation tasks.