TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction
This work proposes TrustCLIP, a reconstruction-driven framework that treats a feature-conditioned generator as an explicit privacy adversary and learns a projection between encoder features and downstream modules that is explicitly optimized to degrade the reconstructions produced by generative attackers while retainin...