Noise-Resilient Clinical Image Generation: Benchmarking Adversarial Robustness of Diffusion Models versus StyleGAN2

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@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},
      doi = {https://doi.org/10.64388/IREV7I4-1722524}
  }