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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11005974/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |