DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models

Understand

Recent text-to-image diffusion models have shown surprising performance in generating high-quality images.

• However, concerns have arisen regarding the unauthorized data usage during the training or fine-tuning process.
• One example is when a model trainer collects a set of images created by a particular artist and attempts to train a model capable of generating similar images without obtaining permission and giving credit to the artist.
• To address this issue, we propose a method for detecting such unauthorized data usage by planting the injected memorization into the text-to-image diffusion models trained on the protected dataset.