Boyu Kuang

When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

This work reveals, for the first time, that RSMT can substantially amplify privacy leakage of these real training samples, and proposes a lightweight leakage propensity indicator computable from real data alone that reliably identifies high-risk datasets unsuitable for entering RSMT, as a self-assessable mitigation.