mlsanityData to 7 Oct 2026

Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks

arXiv 2407.15526 ↗First posted 22 Jul 2024cs.CV cs.AI cs.CRcited by 5

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

Generative artificial intelligence has transformed the generation of synthetic data, providing innovative solutions to challenges like data scarcity and privacy, which are particularly critical in fields such as medicine. However, the effective use of this synthetic data to train high-performance models remains a significant challenge. This paper addresses this issue by introducing Knowledge Recycling (KR), a pipeline designed to optimise the generation and use of synthetic data for training downstream classifiers. At the heart of this pipeline is Generative Knowledge Distillation (GKD), the proposed technique that significantly improves the quality and usefulness of the information provided to classifiers through a synthetic dataset regeneration and soft labelling mechanism. The KR pipeline has been tested on a variety of datasets, with a focus on six highly heterogeneous medical image datasets, ranging from retinal images to organ scans. The results show a significant reduction in the performance gap between models trained on real and synthetic data, with models based on synthetic data outperforming those trained on real data in some cases. Furthermore, the resulting models show almost complete immunity to Membership Inference Attacks, manifesting privacy properties missing in models trained with conventional techniques.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 32 of its 49 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

32 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Membership Inference Attacks From First Principles7 Dec

Other references · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  7. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  8. 2016Improved Techniques for Training GANscited by 2,938
  9. 2016Deep Learning with Differential Privacycited by 2,682
  10. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  11. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  12. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
19 more
  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  2. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  3. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  4. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  5. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  6. 2024Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillationcited by 276
  7. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  8. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
  9. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  10. 2019Classification Accuracy Score for Conditional Generative Modelscited by 134
  11. 2023Privacy Auditing with One (1) Training Runcited by 119
  12. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
  13. 2019This dataset does not exist: training models from generated imagescited by 65
  14. 2024Consistency-diversity-realism Pareto fronts of conditional image generative modelscited by 35
  15. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  16. 2023Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniquescited by 11
  17. 2022On the utility and protection of optimization with differential privacy and classic regularization techniquescited by 10
  18. 2024Stable Diffusion Dataset Generation for Downstream Classification Taskscited by 9
  19. 2023Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networkscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharingcited by 7
  2. 2025Your Image Generator Is Your New Private Datasetcited by 2
  3. 2026PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensemblescited by 1
  4. 2026Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusioncited by 1
  5. 2025Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generationcited by 0