  Current text-to-image diffusion models have achieved groundbreaking results
in image generation tasks. However, the unavoidable inclusion of sensitive
information during pre-training introduces significant risks such as copyright
infringement and privacy violations in the generated images. Machine Unlearning
(MU) provides a effective way to the sensitive concepts captured by the model,
has been shown to be a promising approach to addressing these issues.
Nonetheless, existing MU methods for concept erasure encounter two primary
bottlenecks: 1) generalization issues, where concept erasure is effective only
for the data within the unlearn set, and prompts outside the unlearn set often
still result in the generation of sensitive concepts; and 2) utility drop,
where erasing target concepts significantly degrades the model's performance.
To this end, this paper first proposes a concept domain correction framework
for unlearning concepts in diffusion models. By aligning the output domains of
sensitive concepts and anchor concepts through adversarial training, we enhance
the generalizability of the unlearning results. Secondly, we devise a
concept-preserving scheme based on gradient surgery. This approach alleviates
the parts of the unlearning gradient that contradict the relearning gradient,
ensuring that the process of unlearning minimally disrupts the model's
performance. Finally, extensive experiments validate the effectiveness of our
model, demonstrating our method's capability to address the challenges of
concept unlearning in diffusion models while preserving model utility.
