Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis
Deep learning models have achieved groundbreaking results in computer vision; however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11005974/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714388919910400 |
|---|---|
| author | Jaesung Shim Kyuri Jo |
| author_facet | Jaesung Shim Kyuri Jo |
| author_sort | Jaesung Shim |
| collection | DOAJ |
| description | Deep learning models have achieved groundbreaking results in computer vision; however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep learning models. This paper proposes a simple, powerful, and efficient adversarial defense method: a Siamese network-based Denoising Autoencoder (Siamese-DAE). This method addresses the reduction in classification accuracy caused by the denoising process. Experiments on Chest X-ray, Brain MRI, Retina, and Skin images, using FGSM, PGD, DeepFool, CW, SPSA, and AutoAttack adversarial algorithms, demonstrate that the Siamese-DAE, trained to remove noise, effectively eliminates perturbations, leading to improved classification accuracy compared not only to the standard classification model but also to relevant denoising defense models. |
| format | Article |
| id | doaj-art-d1728c8ede5b409d99fd3fd94f3d6b9b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d1728c8ede5b409d99fd3fd94f3d6b9b2025-08-20T03:13:43ZengIEEEIEEE Access2169-35362025-01-0113863338634310.1109/ACCESS.2025.357089811005974Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image AnalysisJaesung Shim0https://orcid.org/0009-0009-1464-8127Kyuri Jo1https://orcid.org/0000-0001-9222-6346Department of Computer Engineering, Chungbuk National University, Cheongju, Republic of KoreaDepartment of Computer Engineering, Chungbuk National University, Cheongju, Republic of KoreaDeep learning models have achieved groundbreaking results in computer vision; however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep learning models. This paper proposes a simple, powerful, and efficient adversarial defense method: a Siamese network-based Denoising Autoencoder (Siamese-DAE). This method addresses the reduction in classification accuracy caused by the denoising process. Experiments on Chest X-ray, Brain MRI, Retina, and Skin images, using FGSM, PGD, DeepFool, CW, SPSA, and AutoAttack adversarial algorithms, demonstrate that the Siamese-DAE, trained to remove noise, effectively eliminates perturbations, leading to improved classification accuracy compared not only to the standard classification model but also to relevant denoising defense models.https://ieeexplore.ieee.org/document/11005974/Adversarial examplesdenoising autoencoderSiamese networkdeep learning |
| spellingShingle | Jaesung Shim Kyuri Jo Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis IEEE Access Adversarial examples denoising autoencoder Siamese network deep learning |
| title | Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis |
| title_full | Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis |
| title_fullStr | Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis |
| title_full_unstemmed | Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis |
| title_short | Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis |
| title_sort | siamese denoising autoencoders for enhancing adversarial robustness in medical image analysis |
| topic | Adversarial examples denoising autoencoder Siamese network deep learning |
| url | https://ieeexplore.ieee.org/document/11005974/ |
| work_keys_str_mv | AT jaesungshim siamesedenoisingautoencodersforenhancingadversarialrobustnessinmedicalimageanalysis AT kyurijo siamesedenoisingautoencodersforenhancingadversarialrobustnessinmedicalimageanalysis |