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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.”
From Medical imaging deep learning with differential privacy (2021) · cites this paper as [24]
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Cites
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Research works printed but not linked: 8
- [3] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein GAN. arXiv 1701.07875, 2017. · arXiv
- [20] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014.
- [31] D. P. Kingma and M. Welling. Auto-Encoding Variational Bayes. In ICLR, 2013.
- [37] Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen. Understanding Membership Inferences on Well-Generalized Learning Models. arXiv:1802.04889, 2018. · arXiv
- [41] L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov. Inference Attacks Against Collaborative Learning. arXiv:1805.04049, 2018. · arXiv
- [46] N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar. Semi-supervised knowledge transfer for deep learning from private training data. In ICLR, 2017.
- [62] A. Triastcyn and B. Faltings. Generating differentially private datasets using gans. arXiv preprint arXiv:1803.03148, 2018. · arXiv
- [67] X. Wu and X. Zhang. Automated Inference on Criminality using Face Images. arXiv 1611.04135, 2016. · arXiv
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(2025) · Elsevier eBooks · doi:10.1016/b978-0-44-324840-5.00019-6
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Qiu, R., Luo, Z., He, Q., Zhou, Y., Kang, S., & Wei, C. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70398
Intrusion Detection System Based on Improved CART Decision Tree Algorithm
Tan, Q. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-7991-1_46
Modeling and Security Analysis of Attacks on Machine Learning Systems
Singhal, A. (2025) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi67134.2025.00061
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
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