提出PRSS方法，在保护隐私的同时提升图像生成质量。
Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models
- 通过提示重锚定与语义搜索优化扩散模型生成过程
- 在不同隐私等级下均实现隐私与效果的双重提升
- 适合关注生成内容原创性与合规性的研究人员
文本到图像的扩散模型虽能精准生成符合用户提示的图像，但容易记忆训练数据中的图像，引发原创性争议和隐私问题，尤其当训练数据含专有内容时可能带来法律风险。尽管已有缓解方法，但增强隐私常导致输出质量显著下降，表现为文本对齐分数降低。为此，本文提出新的PRSS方法，改进无分类器引导机制，融合提示重锚定（PR）以增强隐私，结合语义提示搜索（SS）以提升生成质量。在多种隐私水平下的大量实验表明，该方法持续优化隐私-效用权衡，达到当前最佳表现。
原文摘要 · Abstract (English)
Text-to-image diffusion models have demonstrated remarkable capabilities in creating images highly aligned with user prompts, yet their proclivity for memorizing training set images has sparked concerns about the originality of the generated images and privacy issues, potentially leading to legal complications for both model owners and users, particularly when the memorized images contain proprietary content. Although methods to mitigate these issues have been suggested, enhancing privacy often results in a significant decrease in the utility of the outputs, as indicated by text-alignment scores. To bridge the research gap, we introduce a novel method, PRSS, which refines the classifier-free guidance approach in diffusion models by integrating prompt re-anchoring (PR) to improve privacy and incorporating semantic prompt search (SS) to enhance utility. Extensive experiments across various privacy levels demonstrate that our approach consistently improves the privacy-utility trade-off, establishing a new state-of-the-art.
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