Feature Machine Unlearning in Diffusion Models.

The diffusion model has gained considerable attention as an advanced deep generation model, showcasing remarkable performance. However, concerns have emerged regarding privacy and equity due to potential model misuse, especially for some sensitive features such as gender. Our focus is on addressing the challenge of machine unlearning within an unconditional diffusion model that selectively omits specific features. Our objective is to modify images generated by a pre-trained model by selectively removing particular image components. To achieve this, we propose a novel learning framework for unconditional image-to-image diffusion models. This framework integrates a scoring model into a pre-trained diffusion model, allowing for refinement without the need for a complete retraining process. Experimental validation using datasets such as MNIST and CelebA demonstrates the effectiveness of our approach. It highlights the model’s capability to generate and remove target features while maintaining the fidelity of the original image.