1. bookVolume 2019 (2019): Issue 1 (January 2019)
Journal Details
License
Format
Journal
eISSN
2299-0984
First Published
16 Apr 2015
Publication timeframe
4 times per year
Languages
English
access type Open Access

LOGAN: Membership Inference Attacks Against Generative Models

Published Online: 24 Dec 2018
Volume & Issue: Volume 2019 (2019) - Issue 1 (January 2019)
Page range: 133 - 152
Received: 31 May 2018
Accepted: 16 Sep 2018
Journal Details
License
Format
Journal
eISSN
2299-0984
First Published
16 Apr 2015
Publication timeframe
4 times per year
Languages
English
Abstract

Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator’s capacity to learn statistical differences in distributions. We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects (CIFAR-10), and medical images (Diabetic Retinopathy). We also discuss the sensitivity of the attacks to different training parameters, and their robustness against mitigation strategies, finding that defenses are either ineffective or lead to significantly worse performances of the generative models in terms of training stability and/or sample quality.

Keywords

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