Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images
With the growing adoption of Deep Learning for imaging tasks in biometrics and healthcare, it becomes increasingly important to ensure privacy when using and sharing images of people. Several works enable privacy-preserving image sharing by anonymizing the images so that the corresponding individual...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Signal Processing |
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| Online Access: | https://ieeexplore.ieee.org/document/11054277/ |
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| author | Helena Montenegro Jaime S. Cardoso |
| author_facet | Helena Montenegro Jaime S. Cardoso |
| author_sort | Helena Montenegro |
| collection | DOAJ |
| description | With the growing adoption of Deep Learning for imaging tasks in biometrics and healthcare, it becomes increasingly important to ensure privacy when using and sharing images of people. Several works enable privacy-preserving image sharing by anonymizing the images so that the corresponding individuals are no longer recognizable. Most works average images or their embeddings as an anonymization technique, relying on the assumption that the average operation is irreversible. Recently, cold diffusion models, based on the popular denoising diffusion probabilistic models, have succeeded in reversing deterministic transformations on images. In this work, we leverage cold diffusion to decompose superimposed images, empirically demonstrating that it is possible to obtain two or more identically-distributed images given their average. We propose novel sampling strategies for this task and show their efficacy on three datasets. Our findings highlight the risks of averaging images as an anonymization technique and argue for the use of alternative anonymization strategies. |
| format | Article |
| id | doaj-art-e3c91537f8d1484e962480cb12affc7f |
| institution | Kabale University |
| issn | 2644-1322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj-art-e3c91537f8d1484e962480cb12affc7f2025-08-20T03:50:32ZengIEEEIEEE Open Journal of Signal Processing2644-13222025-01-01678479410.1109/OJSP.2025.358396311054277Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed ImagesHelena Montenegro0https://orcid.org/0000-0001-6237-3011Jaime S. Cardoso1https://orcid.org/0000-0002-3760-2473Faculty of Engineering, University of Porto, Porto, PortugalFaculty of Engineering, University of Porto, Porto, PortugalWith the growing adoption of Deep Learning for imaging tasks in biometrics and healthcare, it becomes increasingly important to ensure privacy when using and sharing images of people. Several works enable privacy-preserving image sharing by anonymizing the images so that the corresponding individuals are no longer recognizable. Most works average images or their embeddings as an anonymization technique, relying on the assumption that the average operation is irreversible. Recently, cold diffusion models, based on the popular denoising diffusion probabilistic models, have succeeded in reversing deterministic transformations on images. In this work, we leverage cold diffusion to decompose superimposed images, empirically demonstrating that it is possible to obtain two or more identically-distributed images given their average. We propose novel sampling strategies for this task and show their efficacy on three datasets. Our findings highlight the risks of averaging images as an anonymization technique and argue for the use of alternative anonymization strategies.https://ieeexplore.ieee.org/document/11054277/Image anonymizationsuperimposed image decompositiondiffusion models |
| spellingShingle | Helena Montenegro Jaime S. Cardoso Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images IEEE Open Journal of Signal Processing Image anonymization superimposed image decomposition diffusion models |
| title | Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images |
| title_full | Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images |
| title_fullStr | Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images |
| title_full_unstemmed | Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images |
| title_short | Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images |
| title_sort | leveraging cold diffusion for the decomposition of identically distributed superimposed images |
| topic | Image anonymization superimposed image decomposition diffusion models |
| url | https://ieeexplore.ieee.org/document/11054277/ |
| work_keys_str_mv | AT helenamontenegro leveragingcolddiffusionforthedecompositionofidenticallydistributedsuperimposedimages AT jaimescardoso leveragingcolddiffusionforthedecompositionofidenticallydistributedsuperimposedimages |