Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
Summary
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.
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Paper page - Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
Source: https://huggingface.co/papers/2606.07271
Abstract
Rectified Flows retain subtle training data traces that accumulate during training and can be exploited for membership inference attacks.
Understanding what generative models retain from training data remains challenging, with implications for copyright and privacy. Beyond verbatim reproduction, models can encode subtler traces of their training data that never surface in their outputs yet remain exploitable. We study this regime forRectified Flows, which are increasingly used in deployed generative systems. We analyse theinterpolation pathX_λ= (1-λ)X_0 + λX_1 that defines the Rectified Flow training. We show that a gap exists between the reconstruction of train and test data that follows abell-shaped curveover λ, wich accumulates during training, while the validation metrics remain stable. The signal has a maximum whose location we derive in closed form underGaussian assumptions. We validate these predictions on both audio and images and show that the bell-shaped structure is universal, while the peak prediction holds when our assumptions are satisfied. As a proof of concept, we exploit this specific λ-resolved structure to perform aMembership Inference Attack, distinguishing members of the training set from non-members.
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