# Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation

arXiv: https://arxiv.org/abs/2303.02559 · HTML: https://mlsanity.com/paper/2303.02559/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [Unlearnable Examples: Making Personal Data Unexploitable](https://mlsanity.com/paper/2101.04898/) (2021)

## Built on by (most cited first, 4 of 4)

- [Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise](https://mlsanity.com/paper/2311.13091/) (2023)
- [Medical Unlearnable Examples: Securing Medical Data from Unauthorized Training via Sparsity-Aware Local Masking](https://mlsanity.com/paper/2403.10573/) (2024)
- [Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Models](https://mlsanity.com/paper/2406.18944/) (2024)
- [Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle](https://mlsanity.com/paper/2608.04314/) (2026)

## Abstract (arXiv)

The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researchers more chances to contribute. However, institutions often hesitate to share their data with the public due to the risk of data exploitation by unauthorized third parties for another commercial usage (e.g., training AI models). This phenomenon might hinder the development of the whole healthcare research community. To address this concern, we propose a novel approach termed `unlearnable biomedical image' for protecting biomedical data by injecting imperceptible but delusive noises into the data, making them unexploitable for AI models. We formulate the problem as a bi-level optimization and propose three kinds of anti-learning perturbation generation approaches to solve the problem. Our method is an important step toward encouraging more institutions to contribute their data for the long-term development of the research community.