A Method for Detecting Distinctive Patterns of Real Patients in Generated Images
Generative diffusion models are a well-established method for generating high-quality images. However, there are studies that show that diffusion models are less privacy-friendly than generative models, such as generative adversarial networks and a growing family of their modifications. The discover...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Educational institution «Belarusian State University of Informatics and Radioelectronics»
2025-02-01
|
| Series: | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
| Subjects: | |
| Online Access: | https://doklady.bsuir.by/jour/article/view/4061 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Generative diffusion models are a well-established method for generating high-quality images. However, there are studies that show that diffusion models are less privacy-friendly than generative models, such as generative adversarial networks and a growing family of their modifications. The discovered vulnerabilities require in-depth study of various security aspects. This is especially important for sensitive areas such as medical image analysis tasks and their practical applications. The paper describes a method for detecting image patterns presented in generated images that can potentially be identified in real CT images of patients with pulmonary tuberculosis. The method includes the following main procedures: correlation of pairs of generated and real images to pre-select pairs that involve further analysis; calculation of correlation statistics using direct and inverse Fisher transforms; performing affine image registration and calculating pairwise similarity scores; nonlinear (elastic) image registration and recalculation of similarity scores to highlight the most similar/dissimilar image areas. |
|---|---|
| ISSN: | 1729-7648 |