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

• URL: http://arxiv.org/abs/2302.07801v2
• Date: Sat, 5 Aug 2023 23:06:03 GMT
• Title: Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
• Authors: Derui Zhu, Dingfan Chen, Jens Grossklags, Mario Fritz
• Abstract summary: We develop a systematic analysis of membership inference attacks on diffusion models and propose novel attack methods tailored to each attack scenario. Our approach exploits easily obtainable quantities and is highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in realistic scenarios.
• Score: 62.16582309504159
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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