MGE: A Membership Guided Data Extraction Attack against Diffusion Models

Summary

Researchers propose MGE, a membership guided extraction framework that recovers training data from a pre-trained diffusion model. The method treats extraction as conditional generation, with the condition supplied by a membership inference classifier, and adds adversarial gradient and parallel branch decoding to raise success rate and efficiency. On image generation benchmarks, MGE extracts hundreds of images, which the authors say calls for more attention to privacy-preserving diffusion models.

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