Cited by 433Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataDigital Media Forensic Detection

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LOGAN: Membership Inference Attacks Against Generative Models

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Hayes, Jamie, Melis, Luca, Danezis, George, De Cristofaro, Emiliano·2019·Proceedings on Privacy Enhancing Technologies

DOI 10.2478/popets-2019-0008

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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.

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Zhang, B., Yu, R., Sun, H., Li, Y., Xu, J., & Wang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08855 · Cited by 15
A Comprehensive Analysis of Information Leakage in Deep Transfer LearningPreprint
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Robust Federated Learning for Ubiquitous Computing through Mitigation of Edge-Case Backdoor Attacks
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On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification
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Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data
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[Retracted] Medicolite‐Machine Learning‐Based Patient Care Model
Khan, R., Srivastava, A. K., Gupta, M., Kumari, P., & Kumar, S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8109147 · Cited by 11
Retracted
On Memorization in Probabilistic Deep Generative ModelsPreprint
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Generative Adversarial Networks Unlearning
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Authors: Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E.
Affiliations: University College London (GB)
University College London
ORCID: Hayes, Jamie
Melis, Luca
Danezis, George
Venue: Proceedings on Privacy Enhancing Technologies
Publisher: Privacy Enhancing Technologies Symposium Advisory Board
Published: 2018-12-24
Volume: 2019
Issue: 1
Pages: 133-152
ISSN: 2299-0984 (ISSN-L)
DOI: 10.2478/popets-2019-0008
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: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/3.0 (unspecified)
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

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LOGAN: Membership Inference Attacks Against Generative Models

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