Set-Membership Inference Attacks using Data Watermarking

• URL: http://arxiv.org/abs/2307.15067v1
• Date: Thu, 22 Jun 2023 06:47:56 GMT
• Title: Set-Membership Inference Attacks using Data Watermarking
• Abstract summary: We propose a set-membership inference attack for generative models using deep image watermarking techniques. 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.
• Score: 21.69169876101301
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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