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This paper studies membership inference attacks on Rectified Flows by analyzing the interpolation path, revealing a bell-shaped gap between train and test data reconstruction that accumulates during training.
This paper introduces GEM, a concept erasure framework for Rectified Flow models that combines trajectory-based unlearning with teacher-guided flow matching, achieving 5× faster and safer content suppression while preserving benign generation.