Set-Membership Inference Attacks using Data Watermarking
Mike Laszkiewicz Denis Lukovnikov Johannes Lederer Asja Fischer
In this work, we propose a set-membership inference attack for generative
models using deep image watermarking techniques. In particular, we demonstrate
how conditional sampling from a generative model can reveal the watermark that
was injected into parts of the training data. Our empirical results demonstrate
that the proposed watermarking technique is a principled approach for detecting
the non-consensual use of image data in training generative models.