SynthProv: Interpretable Framework for Profiling Identity Leakage

Generative Adversarial Networks (GANs) can generate hyperrealistic face images of synthetic identities based on a latent understanding of real images from a large training set. Despite their proficiency, the term "synthetic identity"remains ambiguous, and the uniqueness of the faces GANs produce is rarely assessed. Recent studies have found that identities from the training data can unintentionally appear in the faces generated by StyleGAN2, but the cause of this phenomenon is unclear. In this work, we propose a novel framework, SynthProv, that utilizes the improved interpolation ability of StyleGAN2 latent space and employs image composition to analyze leakage. This is the first method that goes beyond detection and traces the source or provenance of constituent identity signals in the generated image. Experiments show that SynthProv succeeds in both detection and provenance tasks using multiple matching strategies. We identify identities from FFHQ and CelebA-HQ training datasets with the highest leakage into the latent space as "leaking reals". Analyzing latent space behavior to evaluate generative model privacy via leakage is an important research direction, as undetected leaking reals pose a significant threat to training data privacy. Our code is available at https://github.com/jaisidhsingh/SynthProv.