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!
|
| _version_ | 1849772733013950464 |
|---|---|
| author | V. A. Kovalev |
| author_facet | V. A. Kovalev |
| author_sort | V. A. Kovalev |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d615b99442d4ea2aceeb7fea9e36f00 |
| institution | DOAJ |
| issn | 1729-7648 |
| language | Russian |
| publishDate | 2025-02-01 |
| publisher | Educational institution «Belarusian State University of Informatics and Radioelectronics» |
| record_format | Article |
| series | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
| spelling | doaj-art-2d615b99442d4ea2aceeb7fea9e36f002025-08-20T03:02:15ZrusEducational institution «Belarusian State University of Informatics and Radioelectronics»Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki1729-76482025-02-01231475310.35596/1729-7648-2025-23-1-47-532049A Method for Detecting Distinctive Patterns of Real Patients in Generated ImagesV. A. Kovalev0The United Institute of Informatics Problems of the National Academy of Sciences of BelarusGenerative 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.https://doklady.bsuir.by/jour/article/view/4061diffusion generative modelscomputed tomographyprivacy preserving |
| spellingShingle | V. A. Kovalev A Method for Detecting Distinctive Patterns of Real Patients in Generated Images Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki diffusion generative models computed tomography privacy preserving |
| title | A Method for Detecting Distinctive Patterns of Real Patients in Generated Images |
| title_full | A Method for Detecting Distinctive Patterns of Real Patients in Generated Images |
| title_fullStr | A Method for Detecting Distinctive Patterns of Real Patients in Generated Images |
| title_full_unstemmed | A Method for Detecting Distinctive Patterns of Real Patients in Generated Images |
| title_short | A Method for Detecting Distinctive Patterns of Real Patients in Generated Images |
| title_sort | method for detecting distinctive patterns of real patients in generated images |
| topic | diffusion generative models computed tomography privacy preserving |
| url | https://doklady.bsuir.by/jour/article/view/4061 |
| work_keys_str_mv | AT vakovalev amethodfordetectingdistinctivepatternsofrealpatientsingeneratedimages AT vakovalev methodfordetectingdistinctivepatternsofrealpatientsingeneratedimages |