  The widespread use of face recognition technology has given rise to privacy
concerns, as many individuals are worried about the collection and utilization
of their facial data. To address these concerns, researchers are actively
exploring the concept of ``unlearnable examples", by adding imperceptible
perturbation to data in the model training stage, which aims to prevent the
model from learning discriminate features of the target face. However, current
methods are inefficient and cannot guarantee transferability and robustness at
the same time, causing impracticality in the real world. To remedy it, we
propose a novel method called Segue: Side-information guided generative
unlearnable examples. Specifically, we leverage a once-trained multiple-used
model to generate the desired perturbation rather than the time-consuming
gradient-based method. To improve transferability, we introduce side
information such as true labels and pseudo labels, which are inherently
consistent across different scenarios. For robustness enhancement, a distortion
layer is integrated into the training pipeline. Extensive experiments
demonstrate that the proposed Segue is much faster than previous methods
(1000$\times$) and achieves transferable effectiveness across different
datasets and model architectures. Furthermore, it can resist JPEG compression,
adversarial training, and some standard data augmentations.
