Diffusion models have demonstrated remarkable capabilities in image synthesis, but their recently proven vulnerability to Membership Inference Attacks (MIAs) poses a critical privacy concern.
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Feb 2, 2023 · In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern.
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In this paper, we investigate the vulnerability of diffusion models to Member- ship Inference Attacks (MIAs), a common pri- vacy concern.
Jul 23, 2023 · Our results indicate that existing MIAs designed for GANs or VAE are largely ineffective on diffusion models, either due to inapplicable ...
This is the official implementation of the paper "Are Diffusion Models Vulnerable to Membership Inference Attacks?".
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Step-wise Error Comparing Membership Inference (SecMI) is proposed, a query-based MIA that infers memberships by assessing the matching of forward process ...
Here, we demonstrate a privacy vulnerability of diffusion models through a \emph{membership inference (MI) attack}, which aims to identify whether a target ...
Sep 27, 2024 · Diffusion models have demonstrated remarkable capabilities in image synthesis, but their recently proven vulnerability to Membership ...
In this paper, we investigate whether a diffusion model is resistant to a membership inference attack, which evaluates the privacy leakage of a machine ...
[PDF] Towards More Realistic Membership Inference Attacks on Large ...
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As shown in [4], better diffusion models are more vulnerable to membership inference attacks: the quality of the gener- ated samples is proportional to the ...