Membership is Ownership: A Robust Ownership Verification Framework for Diffusion Models

• URL: http://arxiv.org/abs/2608.28929v1
• Date: Fri, 28 Aug 2026 22:56:52 GMT
• Title: Membership is Ownership: A Robust Ownership Verification Framework for Diffusion Models
• Abstract summary: Large-scale diffusion models have fueled numerous profitable downstream applications for AI-related businesses.<n>These models are deemed valuable intellectual property (IP) for tech companies like OpenAI and Google.<n>This work investigates IP protection for AI models in a realistic commercial scenario with minimal model utility loss.
• Score: 14.732690805955876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale diffusion models have fueled numerous profitable downstream applications for AI-related businesses, including visual editing and content creation. Meanwhile, due to the huge amount of resource consumption (e.g., computation and high-quality data) during training, such diffusion models are deemed valuable intellectual property (IP) for tech companies like OpenAI and Google. Yet, the IP assets are vulnerable to various unauthorized uses by adversaries seeking to steal models for customized, usually commercial applications. Some existing approaches have explored IP protection for AI models; however, they mostly face structural limitations in common --- using a training-time watermarking by injecting artifacts in the model, which can impose a measurable utility cost and can be weakened by post-hoc fine-tuning. To address these challenges, this work investigates IP protection (i.e., model ownership verification) for diffusion models in a realistic commercial scenario with minimal model utility loss. Specifically, the proposed method builds a framework for model ownership verification, termed ``{Membership is Ownership} (MiO)'', based on a population-level hypothesis test on a private member evidence dataset. MiO verifies ownership using two criteria: model attribution through membership inference and model separation from public references. Both are tested at $p<10^{-6}$. We evaluate MiO on DDIM and Stable Diffusion models without modifying the owner model or its sampling pipeline, and report ROC-AUC and true-positive rates at fixed nominal false-positive targets. Furthermore, MiO stays stable under different post-theft fine-tuning and weight perturbation in adversarial scenarios, reflecting better robustness compared to the watermarking methods.

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