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LOGAN: Membership Inference Attacks Against Generative Models
Open accessAbstract
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.
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“Formal security and secrecy mechanisms such as model encryption can only partially address this requirement, as even encrypted models have been found to leak sensitive information in previous work23,24.”
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Metadata
- Authors
- Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E.
- Affiliations
- University College London (GB)
- University College London
- Venue
- Proceedings on Privacy Enhancing Technologies
- Published
- 2018-12-24
- Volume
- 2019
- Issue
- 1
- Pages
- 133-152
- ISSN
- 2299-0984 (ISSN-L)
- OpenAlex
- W2887995258
- Cited by
- 433
- References
- 64
- Citations by year
- 2026: 31 · 2025: 46 · 2024: 62 · 2023: 74 · 2022: 69 · 2021: 68 · 2020: 54 · 2019: 27 · 2018: 2
- FWCI
- 21.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings on Privacy Enhancing Technologies (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- membership inference attacks, generative models, generative adversarial networks, privacy attacks, overfitting detection, black-box attacks, white-box attacks, defense robustness, training data privacy, medical image privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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