Generative Adversarial Networks Unlearning
Hui Sun Tianqing Zhu Wenhan Chang Wanlei Zhou
As machine learning continues to develop, and data misuse scandals become
more prevalent, individuals are becoming increasingly concerned about their
personal information and are advocating for the right to remove their data.
Machine unlearning has emerged as a solution to erase training data from
trained machine learning models. Despite its success in classifiers, research
on Generative Adversarial Networks (GANs) is limited due to their unique
architecture, including a generator and a discriminator. One challenge pertains
to generator unlearning, as the process could potentially disrupt the
continuity and completeness of the latent space. This disruption might
consequently diminish the model's effectiveness after unlearning. Another
challenge is how to define a criterion that the discriminator should perform
for the unlearning images. In this paper, we introduce a substitution mechanism
and define a fake label to effectively mitigate these challenges. Based on the
substitution mechanism and fake label, we propose a cascaded unlearning
approach for both item and class unlearning within GAN models, in which the
unlearning and learning processes run in a cascaded manner. We conducted a
comprehensive evaluation of the cascaded unlearning technique using the MNIST
and CIFAR-10 datasets. Experimental results demonstrate that this approach
achieves significantly improved item and class unlearning efficiency, reducing
the required time by up to 185x and 284x for the MNIST and CIFAR-10 datasets,
respectively, in comparison to retraining from scratch. Notably, although the
model's performance experiences minor degradation after unlearning, this
reduction is negligible when dealing with a minimal number of images (e.g., 64)
and has no adverse effects on downstream tasks such as classification.