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Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2
Subject area: Science,Engineering and Technology · Area of research: Clinical Image Generation
Abstract
We study adversarial robustness in clinical image generation, benchmarking a denoising diffusion model against StyleGAN2 under projected gradient descent (PGD) and Carlini-Wagner attacks. On lung CT from LIDC-IDRI and brain MRI from ADNI, we quantify how attack strength inflates FID, erodes structural similarity, and drives attack success. The diffusion model exhibits a substantially smaller robustness gap, keeping attack success near 28% where the GAN exceeds 70%.
How to cite this paper
Naomi Feldman, Chen Wei, Ibrahim Diallo "Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 793-798
Naomi Feldman, Chen Wei, Ibrahim Diallo "Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2" Iconic Research And Engineering Journals, vol. 7, no. 4, Oct. 2023
Naomi Feldman, Chen Wei, Ibrahim Diallo (2023). Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2. Iconic Research And Engineering Journals, 7(4).
Naomi Feldman, Chen Wei, Ibrahim Diallo "Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2" Iconic Research And Engineering Journals, vol. 7, no. 4, Oct. 2023.
@article{1722524,
author = {Naomi Feldman, Chen Wei, Ibrahim Diallo},
title = {Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {4},
pages = {793-798},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722524.pdf},
abstract = {We study adversarial robustness in clinical image generation, benchmarking a denoising diffusion model against StyleGAN2 under projected gradient descent (PGD) and Carlini-Wagner attacks. On lung CT from LIDC-IDRI and brain MRI from ADNI, we quantify how attack strength inflates FID, erodes structural similarity, and drives attack success. The diffusion model exhibits a substantially smaller robustness gap, keeping attack success near 28% where the GAN exceeds 70%.},
month = {October},
}