Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.

To overcome these challenges, we propose a G AN-based LA tent S pace S earch attack ( GLASS) that harnesses abundant prior knowledge from public data using advanced StyleGAN technologies. Additionally, we introduce GLASS++ to enhance reconstruction stability.

Specifically, we lay the foundation within the Bayesian framework.