Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy

Understand

Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyrights.

• Membership inference arises in these contexts as a potential auditing method for detecting unauthorized data usage.
• While some efforts have been made on diffusion models, they are not applicable to text-to-image diffusion models due to the high computation overhead and enhanced generalization capabilities.
• In this paper, we first identify a conditional overfitting phenomenon in text-to-image diffusion models, indicating that these models tend to overfit the conditional distribution of images given the corresponding text rather than the marginal distribution of images only.