  Recent advances in generative models trained on large-scale datasets have
made it possible to synthesize high-quality samples across various domains.
Moreover, the emergence of strong inversion networks enables not only a
reconstruction of real-world images but also the modification of attributes
through various editing methods. However, in certain domains related to privacy
issues, e.g., human faces, advanced generative models along with strong
inversion methods can lead to potential misuses. In this paper, we propose an
essential yet under-explored task called generative identity unlearning, which
steers the model not to generate an image of a specific identity. In the
generative identity unlearning, we target the following objectives: (i)
preventing the generation of images with a certain identity, and (ii)
preserving the overall quality of the generative model. To satisfy these goals,
we propose a novel framework, Generative Unlearning for Any Identity (GUIDE),
which prevents the reconstruction of a specific identity by unlearning the
generator with only a single image. GUIDE consists of two parts: (i) finding a
target point for optimization that un-identifies the source latent code and
(ii) novel loss functions that facilitate the unlearning procedure while less
affecting the learned distribution. Our extensive experiments demonstrate that
our proposed method achieves state-of-the-art performance in the generative
machine unlearning task. The code is available at
https://github.com/KHU-AGI/GUIDE.
