Year: 2026
Type: preprint
Abstract: Purpose To test whether disease-aware adversarial attacks can disrupt demographic classification in color fundus photographs (CFPs) while retaining glaucoma classification and whether effects transfer across model architectures. Methods This retrospective study included 13,959 CFPs from 4,271 patients. Vision Transformer (ViT) models classified glaucoma, race, sex, and ethnicity. Standard and disease-aware FGSM, PGD, C&W, and diffusion attacks changed demographic predictions; disease-aware attacks added a glaucoma classification loss term. Fixed ViT-generated perturbations were tested on ResNet50 and EfficientNetB0. Performance was measured using area under the receiver operating characteristic curve (AUC), accuracy, and image-similarity metrics. Results Baseline glaucoma AUCs were 0.958–0.963 and demographic AUCs were 0.955–0.992. Disease-aware attacks better preserved glaucoma classification while strongly altering demographic predictions. Disease-aware PGD preserved sex-cohort glaucoma AUC at 0.909 (95% CI, 0.894–0.922) while sex AUC fell to 0.000 (95% CI, 0.000–0.000). Disease-aware diffusion preserved ethnicity-cohort glaucoma AUC at 0.946 (95% CI, 0.936–0.956) while ethnicity AUC was 0.001 (95% CI, 0.000–0.002). Demographic changes transferred poorly to ResNet50 and EfficientNetB0. Conclusions For PGD, C&W, and diffusion, disease-aware attacks retained more glaucoma classification performance than standard attacks while strongly disrupting targeted demographic prediction. Weak cross-architecture transfer indicates model-specific effects rather than demographic information removal. Translational Relevance Methods intended to reduce sensitive information in retinal images could support multicenter AI development, but failure of one demographic classifier does not establish de-identification. Cross-model evaluation while preserving disease classification may provide a more reliable framework for clinical data sharing.
Source: medRxiv
Authors: Iyad Majid, Mengyu Wang
Institutions: Massachusetts Eye and Ear Infirmary, Harvard University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165911275