Investigating Memorization in Video Diffusion Models
Keywords: Video Diffusion Models, Memorization
Abstract: Diffusion models, widely used for image and video generation, face a significant limitation: the risk of memorizing and reproducing training data during inference, potentially generating unauthorized copyrighted content. While prior research has focused on image diffusion models (IDMs), video diffusion models (VDMs) remain underexplored. To address this, we introduce new metrics specifically designed to separately assess content and motion memorization in VDMs. By applying these metrics, we systematically analyze memorization in various pretrained VDMs, including text-conditional and unconditional models on various datasets, revealing that memorization is widespread across both video and image datasets. Finally, we propose effective detection strategies for both content and motion memorization, offering a foundational approach for improving privacy in VDMs.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 9439
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